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Fig. 1 in Psammophaga fuegia sp. nov., a New Monothalamid Foraminifera from the Beagle Channel, South America
Fig. 1. Map of the Beagle Channel area. The sampling sites are indicated by black dots and their correspondent numbers are shown in groups. Psammophaga fuegia was recovered by microscopy and/or environmental sequencing at fifteen sites that are highlighted by grey arrows. Specimens found in Ushuaia were sampled during a previous expedition.
Fig. 5 in Psammophaga fuegia sp. nov., a New Monothalamid Foraminifera from the Beagle Channel, South America
Fig. 5. SEM images of mineral grains found within Psammophaga fuegia specimens from sites 17 (1 and 2) and 56 (3 and 4). Images 1c, 2c, 3b, 3d, and 4b are in BSE mode highlighting density differences. All remaining images are in SE mode. Note different scales of vari- ous images and insets on images of larger scale showing the position of images in smaller scale. Mineral grains analysed for their chemical composition are marked as follows: a – amphibole, c – cordierite, h – hematite, i – ilmenite, f – ferrigehlenite, p – pyroxene, q – quartz, t – titanite, tm – titanoferous magnetite, and z – zircon.
Рис. 2. Некоторые обсΛеΑованные воΑотоки национаΛьного парка «Анюйский»: А — р. Анюй; Б — протока Кыкычен р. Анюй; В — р. Мани; Г — р. Пихца Fig. 2. Some investigated watercourses of the Anyuysky National Park: А — Anyuy River; Б — Kykychen channel of the Anyuy River; В — Mani River; Г — Pikhtsa River in Zoobenthos of salmon rivers in the Anyuysky National Park (Khabarovsky Region, Russia)
Рис. 2. Некоторые обсΛеΑованные воΑотоки национаΛьного парка «Анюйский»: А — р. Анюй; Б — протока Кыкычен р. Анюй; В — р. Мани; Г — р. Пихца Fig. 2. Some investigated watercourses of the Anyuysky National Park: А — Anyuy River; Б — Kykychen channel of the Anyuy River; В — Mani River; Г — Pikhtsa River
Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
FIGURE 14 in A redescription of deep-channel ghost knifefish, Sternarchogiton preto (Gymnotiformes: Apteronotidae), with assignment to a new genus
FIGURE 14 | Sample of diversity of maxilla shape in Apteronotidae, emphasizing the Navajini. Maxillae are illustrated in lateral view and are not drawn to scale.
FIGURE 12 in A redescription of deep-channel ghost knifefish, Sternarchogiton preto (Gymnotiformes: Apteronotidae), with assignment to a new genus
FIGURE 12 | Collection localities of Tenebrosternarchus preto, star indicating type locality. Some points indicate multiple collections or lots from proximate locations.
FIGURE 4 in A redescription of deep-channel ghost knifefish, Sternarchogiton preto (Gymnotiformes: Apteronotidae), with assignment to a new genus
FIGURE 4 | Suspensorium, opercular series, and lower jaw of Tenebrosternarchus preto, MUSM 54617 (198 mm TL) in A. lateral and B. medial view (image reflected). Thin, unstained hard tissue associated with opercular bones shown in light gray. Abbreviations: ang, anguloarticular; ap, autopalatine cartilage; cm, coronomeckelian; d, dentary; enp, endopterygoid; h, hyomandibula; iop, interopercle; m, Meckel's cartilage; mpt, metapterygoid; op, opercle; pop, preopercle; q, quadrate; rar, retroarticular; sym, symplectic.
FIGURE 3 in A redescription of deep-channel ghost knifefish, Sternarchogiton preto (Gymnotiformes: Apteronotidae), with assignment to a new genus
FIGURE 3 | Illustrations of the neurocranium of Tenebrosternarchus preto MUSM 54617 (198 mm TL) in dorsal A., ventral B., and lateral C. views. Cartilage shown in black. Abbreviations: bao, basioccipital; epo, epioccipital; fro, frontal; let, lateral ethmoid; met, mesethmoid; obs, orbitosphenoid; par, parietal; pas, parasphenoid; pro, prootic; pto, pterotic; pts, pterosphenoid; soc, supraoccipital; spo, sphenotic; vet, ventral ethmoid; vom, vomer.
FIGURE 1 in A redescription of deep-channel ghost knifefish, Sternarchogiton preto (Gymnotiformes: Apteronotidae), with assignment to a new genus
FIGURE 1 | Lateral view of live Tenebrosternarchus preto. A. MUSM 54656 (243 mm TL) from the río Amazonas at Iquitos, Peru; B. ANSP 207797 (232 mm TL) from the rio Negro downstream from Barcelos, Brazil; and C. Detail of the head of ANSP 207797 (232 mm TL).
Channel Measurements from Sitarjevec Mine for Propagation Modelling in a Cave Environment
<p>The dataset contains data collected during a measurement campaign using the <a href="https://www.qorvo.com/products/p/DW1000">DecaWave1000 </a>UWB pulse radio module in the <a href="https://rudniksitarjevec.si/en">Sitarjevec</a> mine in Litija, Slovenia. </p> <p>The measurements include complex channel impulse responses (CIRs) collected for 40 predefined positions with the following system parameters:</p> <ul> <li><strong>CONF1:</strong> Channel 4, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF2: </strong>Channel 4, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17;</li> <li><strong>CONF3: </strong>Channel 7, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF4: </strong>Channel 7, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17.</li> </ul> <p><strong>Measurement setup</strong></p> <p>Measurements were performed using the DecaWave1000 UWB pulse radio module in Segment 1 of the mine. Four points (Point A, Point B, Point C, and Point D) were selected for defining the positions of the nodes as shown in Figure 1. Point A denotes the main entrance, the entrance to the measurement segment is represented by Point B, and the beginning and the end of the measurement environment are denoted by Point C, and Point D, respectively. The length of the mine between Point A and Point D is approximately 60 m. </p> <p>The nodes were mounted on stands 1.4 m above the ground, which is not flat. They were placed on a straight line connecting Point C and Point D. The ANCHOR node was positioned in Point C, and the TAG node was moved along the reference line with a step of 1 m up to a maximum distance of 40 m. The nodes were always positioned so the antenna was centred above the reference line. </p> <p><strong>Folder structure</strong></p> <p>The measurements collected for each of the TAG node positions are stored in the corresponding folder. The folders are named m_n, with n=1, ..., 40 corresponding to the TAG's distance to the ANCHOR. Each folder contains four CSV files holding the CIR data measured using the four pre-defined configurations of the UWB system. Each CSV file stores CIRs of approximately 200 repeated measurements as separate records. The CIRs hold complex values of the 156 strongest multipath components corresponding to the taps in the DecaWave1000 accumulator, each of which represents a 1 ns sample interval. </p> <p><strong>Authors</strong></p> <p>Teodora Kocevska, Aleš Simončič, Grega Morano, Tomaž Javornik, and Andrej Hrovat</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p>teodora.kocevska@ijs.si</p>
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities) in New data on the distribution of the Gynaephora (rossii) species group in Northern Yakutia
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities)
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 5. PSD (dB/Hz) vs freaquency (Hz) of each IMF showen in fig 4 in channel C4 (a) and in C3 (b)
<p> In Fig 5, we noted that ocular artifact frequency is generally low around 5Hz with high amplitude. This artifact appears mainly in IMF3 and IMF4. Finally, band power was applied for the new signal. As a last step, the logarithm of the BP is calculated in order to transform the distribution of this feature to a more Gaussian like shape, because the classifiers we used, such as HMMs and SVM assume normally distributed features.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 5b. PSD (dB/Hz) vs freaquency (Hz) of each IMF showen in fig 4 in channel C4 (a) and in C3 (b)
<p>Therefore, the new signal is reconstructed by keeping only the two first IMFs. EMD also allows eliminating the artifacts in the EEG during the recording sessions like eye blinks and eyeball movements. In Fig 5, we noted that ocular artifact frequency is generally low around 5Hz with high amplitude. This artifact appears mainly in IMF3 and IMF4. Finally, band power was applied for the new signal. As a last step, the logarithm of the BP is calculated in order to transform the distribution of this feature to a more Gaussian like shape, because the classifiers we used, such as HMMs and SVM assume normally distributed features.</p>
14 channels Emotiv epoc recordings
<p>We have recorded the data using a commercial 14 channel Emotiv EEG headset from 5 different subjects. Before placing the headset on the scalp, the electrodes are slightly wetted with a saline solution that improves skin contact (higher conductivity). Here, we have performed two types of measurements: brain activity in a resting state with closed eyes and sitting on a comfortable chair while observing a screen flickering at 15 Hz. </p> <p>In the first task, the subjects close their eyes and brain activity is measured during 10 seconds. A similar process is repeated for the flickering task, in which the subject looks at a flashing screen with alternating colors (black and white) at a 15 Hz frequency.</p> <p>Each folder contains the recordings for a different subjects and each file (realization) contains a matrix, in which each column represents a channel and each row represents a sample (128 samples per second).</p>
Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics
<p>The following provides a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, tram and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information.</p> <p>To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset, we also provide the code and context information to allow other researchers to generate their own synthetic datasets.</p>
DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments
<p><strong>DEMAND: Diverse Environments Multichannel Acoustic Noise Database</strong></p> <p>A database of 16-channel environmental noise recordings</p> <p><strong>Introduction</strong></p> <p>Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.</p> <p>Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the <a href="http://catalog.elra.info/product_info.php?products_id=693">AURORA-2</a> corpus, the <a href="http://spandh.dcs.shef.ac.uk/projects/chime/PCC/datasets.html">CHiME</a> background noise data, or the <a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX-92</a> database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.</p> <p>The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.</p> <p><strong>License</strong></p> <p>This work, the audio data and the document describing it, is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/deed.en_CA">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.</p> <p><strong>The data</strong></p> <p>A description of the data and the recording equipment is provided in the file <strong>DEMAND.pdf</strong>. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.</p> <p><strong>Other information</strong></p> <p>The MATLAB scripts listed in the documentation can be found in the file <strong>scripts.zip</strong>.</p> <p><strong>The Authors</strong></p> <p>This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program <a href="http://versamus.inria.fr">VERSAMUS</a>.</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.