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7,523 results for “Annotation”
Drug-interaction annotations over a large set of drug product labels
<p>Drug interaction annotations over a large set of drug product labels. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)</p>
Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Processed data for the manuscript: Cell specificty of regulatory annotations and their genetics effects on gene expression.</p> <p>The code to generate these data is included in the zip-file: regulatoryAnnotations_comparisons-master.zip</p> <p>and at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Individual dataset information:</p> <p>numberOfSegments.dat.gz: Number of segments in each annotation</p> <p>lengthDistributionOfSegments.dat.gz: Length distribution of segments in each annnotation</p> <p>genomeCoverage.dat.gz: Total genome coverage of regions in each annoation</p> <p>overlapFraction.dat.gz: Basepair level overlap between two pairs of annotations</p> <p>annotations_chromatinStateOverlap.dat.gz: Overlap of annotations with chromatin states</p> <p>information.fourcells_enhancer.dat.gz: Information content of the mean enhancer chromatin posterior probability for annotation segments across four cell types</p> <p>information.fourcells_promoter.dat.gz: Information content of the mean promoter chromatin posterior probability for annotation segments across four cell types</p> <p>gwas_enrichment_stats.txt.gz: Enrichment of GWAS SNPs in annotations</p> <p>GTEx_v7.ESI_medianTPM_min0.15.dat.gz : ESI for genes across 50 tissues</p> <p>gtexv7_lcleqtl.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL in annotations</p> <p>gtexv7_lcleqtl.binned_lclESI.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL binned by lclESI in annotations</p> <p>K562.gtexv7_bloodeqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 blood eQTL effect sizes in K562 annotations </p> <p>GM12878.gtexv7_lcleqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 LCL eQTL effect sizes in GM12878 annotations</p> <p>GM12878.dsqtl.prune0.8.maf0.2.ld0.99.annotations.dat.gz : GM12878 DNase QTL effect sizes in GM12878 annotations</p> <p>GM12878.allelicBiasResults.FracRef.downsampled30.annotations.withMAF.dat.gz: GM12878 ATAC-seq allelic bias effect sizes in GM12878 annotations </p> <p> <br> <br> <br> </p>
Arrangement of J.S. Bach's Invention No. 13 in A minor (BWV784) for the DIMI-A synthesizer by Erkki Kurenniemi (1970): An annotated video of the master tape
<p>The video presents an annotated analysis of the master tape of the arrangement of J.S.Bach's invention in a minor (BWV784) for DIMI-A synthesizer (1970) by Erkki Kurenniemi. Upload also includes picture and excel files of analysis material.</p> <p>Analysis is partially published in Lassfolk, K., Suominen, J., & Ojanen, M. (2015). Interaction of Music and Technology: The Music and Musical Instruments of Erkki Kurenniemi. In J. Krysa, & J. Parikka (Eds.), <em>Writing and Unwriting (Media) Art History : Erkki Kurenniemi in 2048</em> (pp. 261-277). [19] (Leonardo Book Series). Cambridge, Mass.: MIT Press.</p> <p>The video is also available for streaming/embedding via https://vimeo.com/278832133</p> <p> </p>
Chardonnay genome assembly and annotations
<p>Annotations relating to the Chardonnay genome assembly (the study is published here: https://doi.org/10.1371/journal.pgen.1007807). The assembly is also available at NCBI: BioProject: PRJNA399599.</p> <p><strong>chardonnay_p-ctg.fasta / chardonnay_h-ctg.fasta:</strong></p> <p>Contig assembly for Chardonnay. chardonnay_p-ctg.fasta = primary contigs (haploid representation), chardonnay_h-ctg.fasta = haplotigs (alt contigs for phased regions in haploid assembly).</p> <p><strong>p-h.maker.gff / p-h.maker.proteins.faa / p-h.maker.transcripts.fna:</strong></p> <p>Maker-predicted gene annotations.</p> <p><strong>p-h.maker.draft-names.tsv / p-h.orthomcl.orthoGroups.tsv / p-h.KEGG.tsv:</strong></p> <p>Draft names (based on UniprotKB blastP hits), OrthoMCL annotations, and KEGG annotations for maker-predicted genes.</p> <p><strong>p-h.repeats.gff:</strong></p> <p>RepeatMasker-based repeat annotations.</p> <p><strong>chardonnay_primary_contigs_chromosome-order.fa / chardonnay_haplotigs_chromosome-order.fa:</strong></p> <p>Contigs placed in chromosome-order (using PN40024 as reference).</p> <p><strong>chardonnay_primary_contig_mappings.tsv / chardonnay_haplotig_mappings.tsv:</strong></p> <p>Mapping coordinates for chromosome-ordered contigs.</p> <p><strong>kmer-based_parentage.primary_contigs.bed / kmer-based_parentage.haplotigs.bed:</strong></p> <p>Parentage assignments using the kmer-based method described in the study.</p> <p><strong>SNP-based_parentage.primary_contigs.bed / SNP-based_parentage.haplotigs.bed:</strong></p> <p>Parentage assignments using SNP-based method (view haplotig assignments against primary contigs) described in the study.</p> <p><strong>p-ctg.gene-expansion-candidates.tsv / h-ctg.gene-expansion-candidates.tsv / p-ctg.gene-expansion-candidates.bed / h-ctg.gene-expansion-candidates.bed:</strong></p> <p>Gene expansion candidates (TSV = 1 row per predicted orthogroup, BED = annotations for viewing).</p> <p><strong>PN_and_CH.FAR2.msa.png: </strong></p> <p>Multi Sequence Alignment for expansion of FAR2-like genes in described in Chardonnay genome assembly publication described in the study.</p>
Enzymes from the BRENDA database annotated with organism growth temperatures and predicted Topt
<p>Experimental as well as predicted organism growth temperatures were used to annotate enzymes from the BRENDA database (doi: 10.1093/nar/gky1048, https://www.brenda-enzymes.org) version 2018.2 (July 2018). The growth temperature annotation can be used as an estimate of the enzymes catalytic optima. In addition to this, the sequence of each enzyme was taken into account to predict catalytic optima, which are more accurate than simply using OGT as an estimate.</p> <p>The "enzyme_ogt_topt.tsv" file is a tab-separated file with the data headers: ec, uniprot_id, domain, organism, ogt, ogt_source, topt and topt_source. The ec column lists enzyme classes. The uniprot_id lists UniProt identifiers. The domain column lists the domain of life (superkingdom), either Archaea, Bacteria, or Eukarya. The organism column lists organism names, with strain designations removed and formatted to lowercase characters with an underscore _ separating the name parts. The ogt column lists, in degrees centigrade, the organism growth temperature. The ogt_source column lists whether experimental growth temperatures or predicted ones were used for the annotation. The topt column lists the enzyme catalytic optimum. Finally, the topt_source column lists whether experimental or predicted topt was used for the annotation.</p> <p>The "brenda_sequences_20180109.fasta" file follows the standard FASTA format and contains the protein sequences for all annotated enzymes. UniProt identifiers are used as a header for each of the sequences.</p> <p> </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>
Assembly, annotation, and read counts for 2016 Sargasso Sea incubations
<p>Data sets for:</p> <p>Lampe RH, Wang S, Cassar N, and Marchetti A. (in prep) Strategies among phytoplankton in response to alleviation of nutrient stress in a subtropical gyre.</p> <p>File descriptions:</p> <ul> <li>site1_clust99.fa.gz - Assembled contigs for site 1 after clustering based on 99% similarity.</li> <li>site2_clust99.fa.gz - Assembled contigs for site 2 after clustering based on 99% similarity.</li> <li>site1_results.tsv.gz - annotations and counts for each contig from site 1 (tab-delimited)</li> <li>site2_results.csv.gz - annotations and counts for each contig from site 2 (tab-delimited)</li> </ul>
A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations
<p>The MAPS dataset is one of the most used benchmark dataset for automatic music transcription. We propose here an updated version of the ground truth MIDI files, containing, on top of the original pitch, onset and offsets, additional annotations.</p> <p>The annotations include:</p> <ul> <li> <p>Tempo curve</p> </li> <li> <p>Time signature</p> </li> <li> <p>Durations of notes in fraction of a quarter note (some of them are approximate)</p> </li> <li> <p>Key signature (always written as the major relative)</p> </li> <li> <p>Separate left and right hand staff</p> </li> <li> <p>Text annotations from the score (tempo indications, coda...).</p> </li> </ul> <p>If you use these annotations in a published research project, please cite:<br> Adrien Ycart and Emmanouil Benetos. “A-MAPS: Augmented MAPS Dataset with Rhythm and Key Annotations” <em>19th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, </em>September 2018, Paris, France.</p> <p>More information is available at: <a href="http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html">http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html</a></p> <p><strong>Version 1.2 (2019-03-11):</strong></p> <ul> <li>Add 3 missing MAPS files to the dataset: MAPS_MUS-chpn-e01_ENSTDkCl.mid, MAPS_MUS-chpn-e01_SptkBGAm.mid, MAPS_MUS-chpn-e01_StbgTGd2.mid. <strong>WARNING:</strong> <strong>They correspond to the same piece as MAPS_MUS-chpn_op10_e01_AkPnCGdD.mid</strong></li> <li>Tempo curve slightly edited to avoid unnatural tempo changes (see <a href="http://c4dm.eecs.qmul.ac.uk/ycart/a-maps.html">this page</a> for more info).</li> </ul> <p><strong>Version 1.1 (2018-09-19):</strong></p> <ul> <li>BUG FIXED: in some files, the time signature, key signature and text annotations used to be incorrectly shifted by one quarter note. This is now fixed.</li> <li>Sustain pedal activations are now the same as in the MAPS dataset, they are all in the "Piano left" track.</li> </ul> <p> </p>
Quantitative phase microscopy timelapse dataset of PNT1A, DU-145 and LNCaP cells with annotated caspase 3,7-dependent and independent cell death
<p>Time-lapse dataset of prostatic cell lines (DU-145, PNT1A, LNCaP) exposed to cell death-inducing compounds (staurosporine, doxorubicin) and black phosphorus. The time-lapse dataset is annotated as follows: (1) cell masks and cell numbers, (2) by cell death type and timepoint of death in the attached xlsx file. This dataset is supplementary to the article:</p> <p>Vicar, T., Raudenska, M., Gumulec, J. <em>et al.</em> The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death. <em>Sci Rep</em> <strong>10, </strong>1566 (2020). <a href="https://doi.org/10.1038/s41598-020-58474-w">https://doi.org/10.1038/s41598-020-58474-w</a></p> <p>Correlative fluorescence microscopy is in a separate dataset <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a></p> <p>Code is available at <a href="https://github.com/tomasvicar/CellDeathDetect">https://github.com/tomasvicar/CellDeathDetect</a></p> <p><strong>Methods</strong></p> <p><em>Cell culture and cultured cell conditions</em><br> LNCaP cell line was established from a lymph node metastase of the hormone-refractory patient and contains a mutation in the AR gene. This mutation creates a promiscuous AR that can bind to different types of steroids. LNCaP cells are AR-positive, PSA-positive, PTEN-negative and harbor wild-type p53 {Skjoth, 2006 #150; Mitchell, 2000 #149}. PNT1A is immortalized non-tumorigenic epithelial cell line. PNT1A cells harbour wild-type p53. However, SV40 induced T-antigen expression inhibits the activity of p53. This cell line had lost the expression of androgen receptor (AR) and prostate-specific antigen (PSA) (Raudenska, 2019). DU-145 cell line is derived from the metastatic site in the brain and contains P223L and V274F mutations in p53. This cell line is PSA and AR-negative and androgen independent (Chappell, 2012). All cell lines used in this study were purchased from HPA Culture Collections (Salisbury, UK). and were cultured in RPMI-1640 medium with 10 % FBS. The medium was supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml). Cells were maintained at 37°C in a humidified (60%) incubator with 5% CO2 (Sanyo, Japan).</p> <p><em>Correlative time-lapse quantitative phase-fluorescence imaging</em></p> <p>QPI and fluorescence imaging were performed by using multimodal holographic microscope Q-PHASE (TESCAN, Brno, Czech Republic). To determine the amount of caspase-3/7 product accumulation, cells were loaded with 2 µM CellEventTM Caspase-3/7 Green Detection Reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol and visualized using FITC 488 nm filter. To detect the cells with a loss of plasma membrane integrity, cells were stained with 1 ug/ml propidium iodide (Sigma Aldrich Co., St. Louis, MO, USA) and visualized using TRITC 542 nm filter. Nuclear morphology and chromatin condensation were analyzed using Hoechst 33342 nuclear staining (ENZO, Lausen, Switzerland) and visualized using DAPI 461 nm filter. Cells were cultivated in Flow chambers μ-Slide I Lauer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions (37°C, humidified air (60%) with 5% CO2) during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). To image enough cells in one field of view, lens Nikon Plan 10/0.30 were chosen. For each cell line and each treatment, seven fields of view were observed with the frame rate 3 mins/frame for 24 or 48 h respectively. Holograms were captured by CCD camera (XIMEA MR4021 MC-VELETA), fluorescence images were captured using ANDOR Zyla 5.5 sCMOS camera. Complete quantitative phase image reconstruction and image processing were performed in Q-PHASE control software. Cell dry mass values were derived according to {Prescher, 2005 #177} and {Park, 2018 #178} from the phase (eq. (1)), where m is cell dry mass density (in pg/μm2), φ is detected phase (in rad), λ is wavelength in μm (0.65 μm in Q-PHASE), and α is specific refraction increment (≈0.18 μm3/pg). All values in the formula except the Phi are constant. Phi (Phase) is the value measured directly by the microscope. Integrated phase shift through a cell is proportional to its dry mass, which enables studying changes in cell mass distribution (Park et al., 2018).</p> <p><strong>File description</strong></p> <p>There are three archives included for particular cell lines:</p> <ul> <li>QPI_annotated_timelapse_DU145.zip for DU-145 cells</li> <li>QPI_annotated_timelapse_PNT1A.zip for PNT1A cells</li> <li>QPI_annotated_timelapse_LNCaP.zip for LNCaP cells</li> </ul> <p>The archive includes of following files:</p> <ul> <li><strong>Tiff with time-lapse</strong> quantitative phase image (32-bit files 600x600px with values in pg/um2 with framerate 1 frame/3minutes with 1.59 px/um), named <em>QPI_cellline_treatment_FOV.tiff</em></li> <li><strong>Tiff file with segmentation</strong> mask for particular cells named <em>mask_cellline_treatment_FOV.tiff</em></li> <li><strong>xlsx table</strong> with cell death type (1 for apoptosis, 2 for necrosis, 3 for ambiguous/surviving) and time of death for representative cell number from mask, named <em>labels_cellline_treatment_FOV.xlsx</em></li> </ul> <p>file naming has following conventions:</p> <ul> <li>cell names: DU145, PNT1A, LNCaP for particular cell line</li> <li>treatments: st, bp, do for staurosporine, black phosphorus and doxorubicin</li> <li>fields of view: 1 to 7</li> </ul> <p>e.g. QPI_DU145_st_4.tif, mask_DU145_st_4.tif, labels_DU145_st_4.xlsx</p> <p>Note that correlative fluorescence images are available at <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a></p>
Fig. 18. Taumacera deusta Thunberg, 1814 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 18. Taumacera deusta Thunberg, 1814, holotype, ♂, 7.5 mm, UUZM. A. Dorsal view. B. Lateral view. C. Frontal view. D. Label. E. Box label 1. F. Box label 2.
Fig. 17. A–I in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 17. A–I. Palaeophylia tricolor (Fabricius, 1781). A–B. Holotype, unsexed, not measured, BMNH. A. Dorsal view. B. Labels. C–F. Lectotype/paralectotype of Crioceris dimidiata Thunberg, 1827, unsexed, 6.0 mm, UUZM. C. Dorsal view. D. Label. E. Lectotype and paralectotype labels. F. Box label. G–I. Syntype of Crioceris tetrapuncta Thunberg, 1787, unsexed, 6.5 mm, UUZM. G. Dorsal view. H. Label. I. Box label.
Fig. 16. A–E in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 16. A–E. Monolepta melanogaster (Wiedemann, 1823) (syntype of Cryptocephalus capensis Thunberg, 1827, ♂, 5.0 mm, UUZM). A. Dorsal view. B. Lateral view. C. Frontal view. D. Label. E. Box label.
Fig. 6 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 6. Miopristis colon (Thunberg, 1821), holotype, ♀, 5.5 mm, UUZM. A. Dorsal view. B. Lateral view. C. ventral view. D. Frontal view. E. Box label.
Fig. 12 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 12. Smeia undata (Thunberg, 1821) comb. nov. A–D. Holotype, ♂, 6.5 mm, UUZM. A. Dorsal view. B. Frontal view. C. Label. D. Box label. E–F. Syntype of Clythra virginea Lacordaire, 1848, ♂, not measured, ZMHB. E. Dorsal view. F. Labels. G–H. Syntype of Melitonoma pictipennis Jacoby, 1898, ♀, not measured, BMNH. G. Dorsal view. H. Labels.
Fig. 9 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 9. Plecomera thunbergii thunbergii (Lacordaire, 1848). A–E. Syntype of Clythra macropus Thunberg, 1821, ♂, 7.5 mm, UUZM. A. Dorsal view. B. Lateral view. C. Frontal view. D. Label. E. Box label. F–G. Syntype of Clythra quadraticollis Lacordaire, 1848, ♂, not measured, BMNH. F. Dorsal view. G. Labels.
Fig. 15. A–I in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 15. A–I. Monolepta bioculata (Fabricius, 1781). A–B. Lectotype, ♀, not measured, BMNH. A. Dorsal view. B. Labels. C. Chrysomela quadrimaculata Goldfuss, 1805 (drawing from the original description). D–G. Lectotype of Cryptocephalus bioculatus Thunberg, 1827, ♂, 5.5 mm, UUZM. D. Dorsal view. E. Label. F. Lectotype label. G. Box label. H. Paralectotype of Cryptocephalus bioculatus Thunberg, 1827, ♂, 4.8 mm, UUZM (actually Monolepta cruciata Guérin-Méneville, 1849). I. Paralectotype of Cryptocephalus bioculatus Thunberg, 1827, ♂, 4.0 mm, UUZM (actually Monolepta signata (Olivier, 1908)).
Fig. 8 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 8. Miopristis stigma (Thunberg, 1821), holotype, ♂, 7.0 mm, UUZM. A. Dorsal view. B. Lateral view. C. Frontal view. D. Label. E. Box label.
Fig. 14. A–D in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 14. A–D. Exosoma lusitanica (Linnaeus, 1767), holotype of Crioceris haemorrhoa Thunberg, 1827, ♂, 8.0 mm, UUZM. A. Dorsal view. B. Frontal view. C. Label. D. Box label. E–J. Megalognatha festiva (Fabricius, 1781). E–F. Holotype of Cistela festiva Fabricius, 1781, ♀, not measured, BMNH. E. Dorsal view. F. Labels. G–J. Holotype of Crioceris virens Thunberg, 1827, ♀, 4.5 mm, UUZM. G. Dorsal view. H. Frontal view. I. Label. J. Box label.
Fig. 7 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 7. Miopristis flexuosa (Thunberg, 1787). A–D. Holotype, ♀, 4.5 mm, UUZM. A. Dorsal view. B. Frontal view. C. Label. D. Box label. E. ♂, 7.0 mm, RSA, Calvinia Nat. Res., NHMB. F. ♀, 4.7 mm, RSA, Calvinia Nat. Res., NHMB.
Fig. 2 in Annotated review of Cryptocephalinae (Clytrini), Synetinae and part of Galerucinae (Coleoptera, Chrysomelidae) described by Carl Peter Thunberg
Fig. 2. Atelechira elegans (Thunberg, 1821), holotype, ♂, 7.5 mm, UUZM. A. Dorsal view. B. Lateral view. C. Frontal view. D. Label. E. Box label.
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.