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FIGURE 7 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 7. Hermannia scabra (L. Koch, 1879), adult, SEM micrographs: A—part of tuberculate prodorsal surface with bothridial and exobothridial setae and bothridium; B—part of tuberculate notogastral surface with notogastral seta d1. Scale bars 20 μm.
FIGURE 23 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 23. Map on distribution of Hermannia scabra (L. Koch, 1879) and H. nodosa Michael, 1888 in the Arctic (designations: dark blue circle—H. scabra, red circle—H. nodosa; full circles—original data, see Appendix, circles with white center—literature data; on Trägardh 1900, 1931; Nordenskiöld 1901; Thor 1937; Hammer 1944, 1955; Sellnick 1949; Dalenius 1950, 1960; Eglitis 1954; Karppinen 1971; Behan 1978; Woas 1978; Gjelstrup & Søchting 1979; Golosova et al. 1983; Colloff 1988; Gjelstrup, Solhøy 1994; Brodo 2000; Eitminaviciute 2003; Krivolutsky et al. 2003; Chernov 2004; Seniczak et al. 2017b); * marks record "Novaya Zemlya" without detalization (Krivolutsky et al. 2003).
FIGURE 4 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 4. Hermannia scabra (Koch, 1879), adult, SEM micrographs: A—dorsal view; B—ventral view. Scale bars 100 μm.
FIGURE 15 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 15. Hermannia scabra (L. Koch, 1879), larva (A–C) and protonymph (D): A—leg I, right, antiaxial view; B—leg II, right, dorsoantiaxial view; C—leg III, left, antiaxial view; D—leg IV, left, antiaxial view. Scale bars 50 μm.
FIGURE 3 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 3. Hermannia scabra (L. Koch, 1879), adult: A—leg I, without trochanter, right, antiaxial view; B—leg II, without trochanter, right, antiaxial view; C—leg III, left, antiaxial view; D—leg IV, right, antiaxial view. Scale bar 100 μm.
FIGURE 12 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 12. Hermannia scabra (L. Koch, 1879), tritonymph: A—dorsal view (legs omitted); B—posterior part of body, lateral view. Scale bar 100 μm.
FIGURE 19 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 19. Hermannia scabra (L. Koch, 1879), tritonymph (A), slightly deformed, and larva (B), SEM micrographs: A—lateral view; B—part of sternal region with folds and epimeral setae 1a and 2a. Scale bars 100 μm (A), 10 μm (B).
FIGURE 11 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 11. Hermannia scabra (L. Koch, 1879), larva (A–C, F, E) and protonymph (D): A—anterior part of body, lateral view (gnathosoma and legs except trochanters omitted); B—posterior part of body, lateral view; C, D—subcapitulum, ventral view; E—palp, left, paraxial view; F—chelicera, right, antiaxial view. Scale bars 100 μm (A, B), 20 μm (C, D, F), 10 μm (E).
FIGURE 1 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 1. Hermannia scabra (L. Koch, 1879), adult: A—dorsal view; B—subcapitulum, ventral view; C—palp, left, antiaxial view; D—chelicera, left, paraxial view. Scale bars 100 μm (A), 50 μm (B, D), 20 μm (C).
FIGURE 22 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 22. Hermannia scabra (L. Koch, 1879), dissected larva (A–D, F, H) and dissected protonymph (E, G), microscope images: A—rostrum; B—part of prodorsal surface with ribs and rostral and lamellar setae; C—bothridial, exobothridial and interlamellar setae and bothridium; D, E—part of gastronotic region with folds, tuberculate swellings of folds and gastronotic setae d1; F—part of gastronotic region with tuberculate-reticulate surface at opening of opisthonotal gland and gastronotic setae f2; G—part of subcapitular mentum with subcapitular setae; H—part of leg femora II with reticulate surface and seta d.
FIGURE 17 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 17. Hermannia scabra (L. Koch, 1879), larva, slightly deformed, SEM micrographs: A—dorsal view; B—ventral view. Scale bars 50 μm (A), 100 μm (B).
FIGURE 14 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 14. Hermannia scabra (Koch, 1879), nymphs: A, B, C—anogenital region of proto-, deuto- and tritonymphs, respectively. Scale bar 50 μm.
FIGURE 21 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 21. Hermannia scabra (L. Koch, 1879), larva (A–C), deutonymph (D–F) and tritonymph (G–I), microscope images: A, D, G—dorsal view; B, E, H—lateral view; C, F, I—ventral view.
FIGURE 5 in Morphological development, distribution and ecology of the arctic oribatid mite Hermannia scabra (Acari: Oribatida: Hermanniidae) and synonymy of Hermannia gigantea
FIGURE 5. Hermannia scabra (L. Koch, 1879), adult, SEM micrographs: A—anterior view; B—posterior view. Scale bars 200 μm (A), 100 μm (B).
Are developers losing interest on my project? An initial study on unstar events on GitHub platform
<p>GitHub is the most popular social coding platform and the world's largest collection of open source software. In this platform, users can star repositories to manifest their interest or satisfaction with the hosted project. However, it is not possible to maintainers of these projects to know whether they are losing the interest of developers from this platform (i.e., developers are removing their stars). In this paper, we present an initial investigation on how often developers remove stars from GitHub projects and the impact on their popularity. Our results show that GitHub projects frequently lose stars, however these events do not have a major influence on their overall popularity since most of them gain more stars than lose stars. We also show that some programming languages retain the interest from developers for more time.</p>
Dateset The "blue eyed" in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups
<p>Dateset The “blue eyed” in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups</p>
Companion double-blind obfuscated package for "A Toolset for Software Development Analytics"
<p>This is a companion package for the paper ""A Toolset for Software Development Analytics", obfuscated to comply with the rules of double-blind review.</p>
Role and practice of research software development at DLR
<p>The deposit contains the results of a survey concerning the role and practice of research software development at the German Aerospace Center (DLR). The survey started end of November 2018 and ended mid-February 2019. We received 773 answers which gave us interesting insights concerning developer demographics, tool usage, documentation, testing as well as software citation at DLR.</p>
GitHub developer behavior and repository evolution dataset
<p>In this work, based on GitHub Archive project and repository mining tools, we process all available data into concise and structured format to generate GitHub developer behavior and repository evolution dataset. With the self-configurable interactive analysis tool provided by us, it will give us a macroscopic view of open source ecosystem evolution.</p>
DCASE 2020 Challenge Task 2 Development Dataset
<p><strong>Description</strong></p> <p>This dataset is the "development dataset" for the <strong>DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" </strong><a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">[task description]</a>. </p> <p>The data comprises parts of <strong>ToyADMOS</strong> and the <strong>MIMII Dataset</strong> consisting of the normal/anomalous operating sounds of six types of toy/real machines. Each recording is a single-channel (proximately) 10-sec length audio that includes both a target machine's operating sound and environmental noise. The following six types of toy/real machines are used in this task:</p> <ul> <li>Toy-car (ToyADMOS)</li> <li>Toy-conveyor (ToyADMOS)</li> <li>Valve (MIMII Dataset)</li> <li>Pump (MIMII Dataset)</li> <li>Fan (MIMII Dataset)</li> <li>Slide rail (MIMII Dataset)</li> </ul> <p> </p> <p><strong>Recording procedure</strong></p> <p>The ToyADMOS consists of normal/anomalous operating sounds of miniature machines (toys) collected with four microphones, and the MIMII dataset consists of those of real-machines collected with eight microphones. Anomalous sounds in these datasets were collected by deliberately damaging target machines. For simplifying the task, we used only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. The sampling rate of all signals has been downsampled to 16 kHz. From ToyADMOS, we used only IND-type data that contain the operating sounds of the entire operation (i.e., from start to stop) in a recording. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. For the details of the recording procedure, please refer to the papers of <a href="https://ieeexplore.ieee.org/document/8937164">ToyADMOS</a> and <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">MIMII Dataset</a>.</p> <p> </p> <p><strong>Data</strong></p> <p>We first define two important terms in this task: Machine Type and Machine ID. Machine Type means the kind of machine, which in this task can be one of six: toy-car, toy-conveyor, valve, pump, fan, and slide rail. Machine ID is the identifier of each individual of the same type of machine, which in the training dataset can be of three or four. Each machine ID's dataset consists of (i) around 1,000 samples of normal sounds for training and (ii) 100-200 samples each of normal and anomalous sounds for the test. The given labels for each training/test sample are Machine Type, Machine ID, and condition (normal/anomaly). Machine Type information is given by directory name, and Machine ID and condition information are given by their respective file names. </p> <p> </p> <p><strong>Directory structure</strong></p> <p>When you unzip the downloaded files from Zenodo, you can see the following directory structure. As described in the previous section, Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as:</p> <ul> </ul> <p>/dev_data</p> <ul> <li>/ToyCar <ul> <li>/train (Only normal data for all Machine IDs are included.) <ul> <li>/normal_id_01_00000000.wav</li> <li>...</li> <li>/normal_id_01_00000999.wav</li> <li>/normal_id_02_00000000.wav</li> <li>...</li> <li>/normal_id_04_00000999.wav</li> </ul> </li> <li>/test (Normal and anomaly data for all Machine IDs are included.) <ul> <li>/normal_id_01_00000000.wav</li> <li>...</li> <li>/normal_id_01_00000349.wav</li> <li>/anomaly_id_01_00000000.wav</li> <li>...</li> <li>/anomaly_id_01_00000263.wav</li> <li>/normal_id_02_00000000.wav</li> <li>...</li> <li>/anomaly_id_04_00000264.wav</li> </ul> </li> </ul> </li> <li>/ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)</li> <li>/fan</li> <li>/pump</li> <li>/slider</li> <li>/valve</li> </ul> <p> </p> <p>The paths of audio files are:</p> <ul> <li>"/dev_data/<Machine_Type>/train/normal_id_<Machine_ID>_[0-9]+.wav"</li> <li>"/dev_data/<Machine_Type>/test/normal_id_<Machine_ID>_[0-9]+.wav"</li> <li>"/dev_data/<Machine_Type>/test/anomaly_id_<Machine_ID>_[0-9]+.wav"</li> </ul> <p>For example, the Machine Type and Machine ID of "/ToyCar/train/normal_id_01_00000000.wav" are "ToyCar" and "01", respectively, and its condition is normal. The Machine Type and Machine ID of "/fan/test/anomaly_id_00_00000000.wav" are "fan" and "00", respectively, and its condition is anomalous.</p> <p> </p> <p><strong>Baseline system</strong></p> <p>A simple baseline system is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_baseline">[URL]</a>. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was created jointly by <strong>NTT Corporation</strong> and <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Publication</strong></p> <p>If you use this dataset, please cite <strong>all the following three papers</strong>:</p> <p>Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc of Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. <a href="https://ieeexplore.ieee.org/document/8937164">[pdf]</a></p> <p>Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">[pdf]</a></p> <p>Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring<em>,"</em> in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Koizumi_3.pdf">[pdf]</a></p> <p><br> <strong>Feedback</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Yuma Koizumi, <a href="mailto:koizumi.yuma@ieee.org">koizumi.yuma@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul> <p> </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.