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FIGURE 2. Typical and atypical roots. A in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 2. Typical and atypical roots. A. Cylindrical root of Asplenium sandersonii (Aspleniaceae) showing prominent root cap and more proximal zone with root-hairs. Scale bar = 250 µm. B–C. Longitudinal sections of Pinguicula root-tips (Lentibulariaceae). B. Pinguicula vulgaris with root-cap. Scale bar = 200 µm. C. Pinguicula moranensis lacking rootcap. Scale bar = 200 µm. D–F. Ribbon-like root of Podostemum distichum (Podostemaceae). D. Cross-section of ribbonlike root. Scale bar = 400 µm. E, F. Two views of root-tip with asymmetrical root cap, shown from slightly above and slightly below. Scale bars = 500 µm. G–H. Ribbon-like root of Ledermanniella spp. (Podostemaceae). G. Ledermanniella bosii (microtome, transversal section) showing adhesive hairs ("root-hairs") along ventral side. Scale bar = 250 µm. H. Ledermanniella bowlingii. Ribbon-like root shown from ventral side, with exogenous lobe and root-tip lacking cap. Scale bar = 2 mm.
FIGURE 1 in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 1. "Leaves" with mixed identities. A. Compound foliage leaf of ash (Fraxinus excelsior: Oleaceae) infected with the gall mite, Aceria fraxinivora (Nalepa, 1909). Unlike typical ash leaves, this leaf is partially doubly pinnate and lacks a terminal leaflet. The leaf tip is occupied by a bud (arrow), which strongly resembles buds found on shoots. X = stem, Y = leaf rachis. B – D. Indotristicha ramosissima (Podostemaceae). B. Drawing of a portion of a long shoot with lateral ramuli: ca. 3 cm long determinate, feather-like appendages in the positions where leaves normally occur. Similar to typical leaves, most ramuli subtend axillary shoots. Scales are found as lateral stem appendages, especially towards the shoot tips. Stars = tips of axillary shoots, each subtended by ramulus. C. Apical region of a ramulus at a young developmental stage (250 µm long). The apical meristem (RP) is elongated and slightly curved. Most of the globular primordia on flank of the apex will grow into linear, bifid scales. Scale bar = 30 µm. D. Cross-section of a ramulus tip with the apical meristem in the centre (RP) surrounded by linear scales consisting of a single cell layer. Scale bar = 100 µm. C, D modified from Rutishauser & Huber (1991).
FIGURE 5 in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 5. Planar representation of the morphospace of shoots, foliage leaves and bracts in flowering plants (after Jeune et al. 2006). The process combinations of watershoot leaves and bracts of the aquatic plant Utricularia foliosa (Lentibulariaceae) are visualized by Principal Components Analysis of the developmental processes shown in Table 1 (see Jeune et al. 2006 for a more detailed explanation). Each point in the morphospace represents an individual plant structure. The process combinations of watershoots (black circle) and leaves (cross) are more similar to each other than either is to the process combination of bracts (black diamond).
FIGURE 3 in Plant structure ontology: How should we label plant structures with doubtful or mixed identities? *
FIGURE 3. Ricinus communis (Euphorbiaceae). Development of complex androecial units inside the calyx of male flowers (calyx removed in Figs. A–E). A. Young male flower bud. Triangular floral apex starting to form primary primordia. Scale bar = 150 µm. B. Next floral developmental stage, showing ca. 11 primary primordia. Scale bar = 80 µm. C, D. Two consecutive stages of androecium development. The primary primordia divide to form secondary and tertiary primordia. Scale bars = 150 µm. E. Young branching stamens seen from the side. Scale bar = 400 µm. F. Portion of mature branched stamen showing primary, secondary and tertiary branches. Note anthers with thecae separated by elongated connective appendages at ends of the distal branches. Scale bar = 1 mm. G. Mature male flower with calyx divided into 5 reflexed lobes. Several branched stamens are shown: the central ones with long stalks, the peripheral ones with short stalks. Scale bar = 3 mm.
FIGURE 4 in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 4. Schematic of a cap for tagging specimen-bearing pins with p-Chips attached to a piece of ferrite to amplify the transponder response. A: Cross-section; B: Overall view; C: Mounting of the cap on a pin.
FIGURE 1 in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 1. Examples of how labels may be stacked below entomological specimens. Important information may be hidden and difficult to access.
FIGURE 3. a in Ultra-small RFID p-Chips on the heads of entomological pins provide an automatic and durable means to track and label insect specimens
FIGURE 3. a)-c) Schematic representation of p-Chips embedded in an epoxy dome and mounted onto entomological pins. d)- e) Photographs of p-Chip-tagged pins; a view from above the pin head (d), and from the side (e).
MAESTRO Synthetic - Multi-Annotator Estimated Strong Labels
<p>The dataset was created for studying estimation of strong labels using crowdsourcing.</p> <p>It contains 20 synthetic audio files created using Scaper, the reference annotation created with Scaper, and the annotation outcome. Annotation was performed using Amazon Mechanical Turk.</p> <p>Audio files contain excerpts of recordings uploaded to freesound.org.(from Urban Sound 8k dataset). Please see FREESOUNDCREDITS.txt for an attribution list. </p> <p>The dataset contains: </p> <ul> <li>audio: the 20 synthetic soundscapes, each 3 min long</li> <li>ground truth: the "true" reference annotation created using Scaper</li> <li>estimated strong labels: the reference annotation created from the crowdsourced data</li> <li>audio tags: the weak labels corresponding to each 10 s segment of the soundscapes, as annotated</li> </ul> <p>For details on the annotation procedure and label processing methodology, see the following paper:</p> <p>Irene Martin Morato, Manu Harju, and Annamaria Mesaros. <em><a href="https://arxiv.org/abs/2107.12089">Crowdsourcing strong labels for sound event detection</a>.</em> In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2021). New Paltz, NY, Oct 2021.</p> <p> </p> <p> </p>
Fig. 23. Type specimens and their original labels. A in Myrmecophilous Pselaphinae (Coleoptera: Staphylinidae) from New Zealand
Fig. 23. Type specimens and their original labels. A) Kenocoelus dimorphus, B) "Dalma" gigantea, C) Neosampa granulata.
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
<p>The dataset is published with:<br> <br> <em>MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021. Punta Cana, Dominican Republic.</em><br> <br> <strong>Documents: </strong>MultiEURLEX comprises 65k EU in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a Label descriptor, e.g., [60, `agri-foodstuffs'], [6006, `plant product'], [1115, `fruit']. The descriptors are also available in 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EURLEX57K, comprising 57k EU laws with the originally assigned gold labels.</p> <p><strong>Languages: </strong>MultiEURLEX covers 23 languages from 7 families. EU laws are published in all official EU languages, except for Irish for resource-related reasons (Read more: https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes the dataset a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek.</p> <p><strong>Multi-granular Labeling: </strong>EUROVOC<strong> </strong>has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We created three alternative sets of labels per document, by replacing each assigned concept by its ancestor from levels 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.</p> <p><strong>Supported Tasks: </strong>Similarly to EURLEX (Chalkidis et al., 2019), MultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EURLEX57K, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (one-to-one) experiments, it can be used to study cross-lingual transfer scenarios, including one-to-many (systems trained in one language and used in other languages with no training data), and many-to-one or many-to-many (systems jointly trained in multiple languages and used in one or more other languages).</p> <p><strong>Data Split and Concept Drift: </strong>MultiEURLEX is chronologically split in training (55k, 1958-2010), development (5k, 2010-2012), test (5k, 2012-2016) subsets, using the English documents. The test subset contains the same 5k documents in all 23 languages. The development subset also contains the same 5k documents in 23 languages, except Croatian. Croatia is the most recent EU member (2013); older laws are gradually translated. For the official languages of the seven oldest member countries, the same 55k training documents are available; for the other languages, only a subset of the 55k training documents is available. Compared to EURLEX57K (Chalkidis et al., 2019), MultiEURLEX is not only larger (8k more documents) and multilingual; it is also more challenging, as the chronological split leads to temporal real-world concept drift across the training, development, test subsets, i.e., differences in label distribution and phrasing, representing a realistic temporal generalization problem (Huang and Paul, 2019; Lazaridou et al., 2021). Recently, Søgaard et al. (2021) showed this setup is more realistic, as it does not overestimate real performance, contrary to random splits (Gorman and Bedrick, 2019).</p>
Fig. 1. Labels. A in The Type of Nicrophorus chryseus Mazokhin-Porshnyakov, 1953 (Coleoptera: Silphidae) Rediscovered and a Commentary on "Accidental Taxonomy"
Fig. 1. Labels. A) Data and determination label of the holotype of Nicrophorus chryseus, B) Data and determination label of co-collected Nicrophorus investigator.
Fig. 8. Primary types and original labels pinned with Abraeus species. A, B in Phylogenetic Placement of Abraeus vividulus Broun and Notes on New Zealand Histeridae (Coleoptera)
Fig. 8. Primary types and original labels pinned with Abraeus species. A, B) A. brouni, C, D) A. brunneus, E, F) A. vividulus.
Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"
<p><strong>Data and code for "A Linear Time Solution to the Labeled Robinson-Foulds Distance Problem"</strong></p> <p>Samuel Briand, Christophe Dessimoz, Nadia El-Mabrouk, Yannis Nevers</p> <p> </p> <p><strong>Experimental data</strong></p> <p>The __ALF\_Output__ directory contains the results obtained from ALF with parameters specified in the paper, as well as additional files generated in the downstream analysis (see below)</p> <p>The __Partitions__ directory contains one directory by partitioning of the 100 species from ALF in nested sets. Each contains three folder and a file.</p> <p> The summary.txt directory report which family are part of the nested set.</p> <p> The Allfamily directory contains the FASTA file of the 100 gene families generated with ALF, with only the species selected in the partition.</p> <p> The Aln directory contains the MSA for each gene family as generated with MAFFT with the selected species set</p> <p> The FTree directory contains the gene tree for each family as generated with FastTree with the selected species set</p> <p>The __Script__ directory containst the files used to generated the data from the ALF directory, as well as downstream analysis</p> <p> </p> <p> </p> <p>To reproduce the results start by runing __rewriteSeq.py__ , which is used for generating the Partitions. It takes as parameter the ALF directory, the directory in which you wish to generate the partitions, and the path to ALF's genomes FASTA files. If the partition file already exist, you can use the -r option to redo the random selection, otherwise it will generate file for the previous random selection. </p> <p>Example command :</p> <p>python rewriteSeq.py -i ../ALF\_output -o ../Partitions -g ../ALF\_output/DB</p> <p> </p> <p>Then, by runing __rewriteTree.py__ you will generated the reference trees used for the RF comparisons, as well as species tree used for each partitions. It takes as parameters the ALF directory , the Partitions directory and the species file of the partitions used to create the reference tree (smallest of all partitions)</p> <p>Example command :</p> <p>python rewriteTree.py -i ../ALF\_output/ -p ../Partitions/ -s ../Partitions/Part10/summary.txt</p> <p> </p> <p>Then, the script __launchFastTree.sh__ will, by partitions, generate a MSA using MAFFT and a phylogenetic tree using FastTree. It takes as parameter the Partitions directory and the number of the identifier of the partition for which you wish tu run it. Notes that the afforementionned software need to be installed before hand.</p> <p>Example command:</p> <p>bash launchFastTree.sh ../Partitions 10</p> <p> </p> <p>Finally, the __LRFAnalysis.ipynb__ file is a Jupyer Notebook used to run downstream analysis of RF and LRF on the different Partitions, including figure generation. Path to the data directory can be set in the 4th block of the Notebook.</p> <p> </p> <p><strong>Comparison of RF, LRF, and ELRF</strong></p> <p> </p> <p>The code to compare is provided as a Jupyter notebook in the directory "Comparison with RF and ELRF". The input NOX4 family from Ensembl version 99 is provided. The output figures are provided as PDF but they can be regenerated by running the notebook.</p> <p> </p>
FIGURE 6. Labels. A in Three new genera of large marsh beetles (Coleoptera: Scirtidae) from Valdivian temperate rain forests of southern South America
FIGURE 6. Labels. A) Solierodes rousseli (Solier), lectotype; B) Mucronotus schwabei (Pic), lectotype; C) M. schwabei (Pic), paralectotype; D) M. schwabei (Pic), paralectotype.
FIGURE 19–21. Ceroderma asperata. 19. Holotype. 20. Type label. 21 in Redescription of Ceroderma asperata Raffray, and description of Cerochusa cilioceps gen. et sp. n. from Hainan, South China (Coleoptera, Staphylinidae, Pselaphinae)
FIGURE 19–21. Ceroderma asperata. 19. Holotype. 20. Type label. 21. Raffray's original habitus illustration. Scale: 1.0 mm.
Species Labels for recordings
<p>The file "SpeciesLabelsForRecordings.xls" contains the names of species (2nd column), found in each recording from zenodo.org/record/49321 (1st column),</p> <p>as well as type of bat-call(3rd column), which can be: Frequency modulated(FM), Constant frequency(CF) or quasi-constant frequency(QCF). Additionally, frequency and duration are mentioned.</p> <p>Species, call-types and additional information are identified by Simone Clemente</p>
Label-free imaging of M1 and M2 macrophage phenotypes in the human dermis in vivo using two-photon excited FLIM
<p>Macrophages (ΜΦs) are important immune effector cells that promote (M1 ΜΦs) or inhibit (M2 ΜΦs) inflammation and are involved in numerous physiological and pathogenic immune responses. Their precise role and relevance, however, are not fully understood for lack of non-invasive quantification methods. Here, we show that two-photon excited fluorescence lifetime imaging (TPE-FLIM), a label-free non-invasive method, can visualize ΜΦs in the human dermis in vivo. We demonstrate in vitro that human dermal ΜΦs exhibit specific TPE-FLIM properties that distinguish them from the main components of the extracellular matrix and other dermal cells. We visualized ΜΦs, their phenotypes and phagocytosis in the skin of healthy individuals in vivo using TPE-FLIM. Additionally, machine learning identified M1 and M2 MФs with a sensitivity of 0.88±0.04 and 0.82±0.03 and a specificity of 0.89±0.03 and 0.90±0.03, respectively. In clinical research, TPE-FLIM can advance the understanding of the role of MФs in health and disease.</p>
FIGURES 1–6. Spinovalva delodectis, adult. 1, 2. Lectotype, female. 1. Dorsal view. 2. Labels. 3, 4. Female, gen. slide 10 in Spinovalva gen. nov., a new genus for Gelechia delodectis Meyrick, 1938 (Lepidoptera, Gelechiidae)
FIGURES 1–6. Spinovalva delodectis, adult. 1, 2. Lectotype, female. 1. Dorsal view. 2. Labels. 3, 4. Female, gen. slide 10/22, O. Bidzilya. 3. Adult, dorsal view. 4. Head, lateral view. 5, 6. Male, gen. slide 9/22, O. Bidzilya. 5. Adult, dorsal view. 6. Head and thorax, ventral view.
FIGURE 1. Yprezethinus grimaldii Holotype AMNH Tad-130. A, Habitus with visible abdominal sternites labelled. B in Transitional morphology and Afrotropical affinity of a bythinoplectine rove beetle from the early Eocene of India (Coleoptera: Staphylinidae: Pselaphinae)
FIGURE 1. Yprezethinus grimaldii Holotype AMNH Tad-130. A, Habitus with visible abdominal sternites labelled. B, Head and pronotum with segments of maxillary palpomeres numbered (MP2–MP4). Bythinoplectina-type lateral excavation, eye, apical pseudosegment of maxillary palp (APs) and mandible (Md) indicated. C, Right antenna with antennomeres numbered. Note the club formed from tightly appressed, hemisphere-shaped antennomeres 10 and 11. D, Close-up view of apically globose tubercles of maxillary palpomeres 3 and 4. E, Proximal leg articulation, showing conically projecting metacoxa (MC), short metatrochanter (MT) and femur (F). F, Right metatarsus (F) with bythinoplectine-type enlarged third tarsomere spanning distance between arrowheads, and minute first visible tarsomere.
aki_labeled_dataset
<p>Dataset provided by Todd Miano for BMIN5030/EPID6000 class project.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.