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793 results for “Startes”
COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms
<p>This dataset release supports the results presented in the paper "COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms" by A. U. Gias and G. Casale, accepted in IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2020.</p> <p>When referring to the dataset please cite the paper above.</p>
WWV 15 MHz reception test -- 60 minutes starting at 1937 UTC, July 25, 2017.
<p>Steven Reyer, WA9VNJ. Approx.Lat/Long: 43.218, -87.951. This 60-minute 15 MHz WWV recording was started at 1937 UTC on July 25, 2017, in preparation for the Solar Eclipse experiment in late August.</p> <p>The radio is a Yaesu FT-857D locked to a Trimble Thunderbolt GPSDO using an XRef-FT oscillator interface. Antenna is an RF-PRO-1B shielded magnetic loop, aimed N-S. Calibration of the radio and sample rate correction is determined using a second Trimble Thunderbolt GPS and Rigol DG1022Z GPS-locked signal generator.</p> <p>Spectrum Lab software is used to record the WAV file and to analyze incoming audio with sample rate of 11025 Hz. FFTs are 512k-point, 75% overlapping, yielding a new measurement every 12 seconds. Radio is tuned to 14999.00 kHz USB yielding audio around 1kHz (labeled "PeakFreq" in the txt file). Thus, the measured radio frequencies are near 15000 kHz. Calibration tests show that the audio frequency measurements are approximately 0.0384 Hz high, due the approximations in both the radio's DDS and the sample rate. This 0.0384 Hz number can be subtracted from the PeakFreq values to get corrected audio frequencies, which can then be added to the 14999 kHz number.</p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
Data for Nemes et al. "Springing forward: Migrating songbirds catch up with the start of spring in North America"
<p>R scripts and data used to estimate bird migration rate and timing relative to spring onset rate and timing within North America.</p>
Error Related Potential at the start of the gait with a lower limb exoskeleton
<h2>Description</h2> <p>This dataset contains EEG signals from experiments designed to evoke Error Related Potentials (ErrP) at the onset of gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using three different stimuli: Tactile, Visual, and VisuoTactile.</p> <p>During the experiment, participants remain stationary and engage in two mental tasks: Relax (R) and Motor Imagery (I) of walking to activate the exoskeleton. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For example, during RC (Relax Correct), the subject maintains an idle state, whereas during RE (Relax Error), the exoskeleton activates unexpectedly. Conversely, in IC, the subject imagines the sensation of starting to walk in their muscles, and the exoskeleton activates, but during IE, the exoskeleton does not move despite the motor imagery. When the exoskeleton activates before starting to walk, the stimulus remains active for 2 seconds to alert the subject about the impending movement. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP in IC, where the stimulus activates but should not evoke an error.</p> <p>Each subject participates in three sessions, one for each stimulus, consisting of 12 trials. In each trial, 10 mental tasks are performed, 5 Relax and 5 Imagination, interleaved. Since the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). In an exception, subject R01_VisuoTactile performed 7 trials of 20 mental tasks each, 10 of each type. However, due to the extended duration of the trials and resulting fatigue, they were later split for subsequent sessions.</p> <p> </p> <h2>Data information</h2> <p>A trial consists of a Matlab structure that stores all information related to the trial experiment. </p> <ul> <li><em>data_EEG</em>: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li><em>data_preprocessed_EEG</em>: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in <em>session.conf.info.preprocessingSteps.ListPreprocessingSteps</em>.</li> <li><em>trigger_EEG</em>: Information related to signal quality and missing data while recording. </li> <li><em>data_EXO</em>: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li><em>data_preprocessed_EXO: </em>The same data recorded by the exoskeleton in <em>data_EXO</em>, since it does not require the application of any filter.</li> <li><em>trigger_EXO</em>: Empty vector. </li> <li><em>data_Actuators</em>: Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li><em>data_preprocessed_Actuators: </em>The same Arduino resposes recorded in <em>data_Actuators</em>, because it does not require any filter application. </li> <li><em>trigger_Actuators</em>: Empty vector. </li> <li><em>task_EEG</em>: Vector that associates a task to each signal sample.</li> <li><em>task_index_EEG</em>: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. </li> <li><em>task_order_EEG</em>: Vector that increments a unit with each task change. </li> <li><em>event_EEG</em>: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li><em>conf</em>: Configuration employed for data acquisition and preprocessing. <ul> <li><em>acquisition</em>: User and signals acquisition information. <ul> <li><em>user_code</em>: User code name.</li> <li><em>feedback</em>: Trial in openloop (User do not have control of the system).</li> <li><em>feedbackErrP</em>: Feedback type employed during the trial.</li> <li><em>readfile</em>: Path to read files after its acquisition.</li> <li><em>saveSession_Script</em>: Script used to save the recorded data.</li> <li><em>writeResults</em>: Path to save the recorded data.</li> <li><em>device</em>: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. </li> <li><em>task</em>: Information about tasks occurring during the trial. <ul> <li><em>task_list</em>: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li><em>sequence_tasks</em>: List of tasks in order of execution.</li> <li><em>sequence_times</em>: List with the duration of each task in the sequence.</li> </ul> </li> <li><em>deviceOutput</em>: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li><em>eye_index</em>: Indexes of EOG electrodes.</li> <li><em>EEG_index</em>: Indexes of EEG electrodes.</li> <li><em>inertial_index</em>: Indexes of inertial electrodes.</li> <li><em>file_name</em>: Trial name.</li> <li><em>num_epochs</em>: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. </li> </ul> </li> <li><em>preadjustment</em>: Empty list. </li> <li><em>preprocessing</em>: Information of the preprocessing filters, parameters and order of application.</li> <li><em>processing</em>: Not necessary for this analysis. </li> <li><em>static</em>: Information used internally by the architecture for its correct operation.</li> <li><em>info</em>: Important information about filters, their order and indexes in <em>data_processed_EEG</em>.</li> </ul> </li> <li><em>times</em>: Struct with information of the devices synchronization and preprocessing times.</li> <li><em>times_processing</em>: Processing duration times. </li> </ul>
F I G U R E 3 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 3 Relationships between fast-start performance, body size (standard length), acclimation temperature, and test temperature in adult mummichogs. Graphs are arranged by performance variable in rows and by test temperature (24, 30, and 36 C) in columns. Colors indicate acclimation temperatures (blue is 24 C, purple is 30 C, and red is 36 C). Lines are the fitted performance–size relationships based on parameter estimates from best-fit mixed-effects models or model-averaged parameter estimates from supported models (see Table 1 and Table S3.). Effects of size on performance are apparent as nonzero slopes within individual graphs; effects of test temperature are evident as variation in average intercepts across test temperature graphs (i.e., comparing average intercepts for acclimation temperature–specific lines across columns within each row of graphs); effects of acclimation temperature are seen as differences in intercepts between acclimation temperature–specific lines within graphs; and interactions between size and temperature are apparent as variations in average slopes (i.e., averaged over acclimation-specific slopes) across test temperature graphs and as differences in acclimation temperature–specific slopes within graphs.
F I G U R E 1 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 1 Morphological scaling in mummichog ranging from YOY to larger adults. Morphological variables are body dimensions (top row) and median fin areas (bottom row), and standard length is a measure of the overall body size. All data were log transformed. Sample sizes for fin areas were reduced because individuals with damaged fins were not measured. Estimates for scaling coefficients (i.e., the slope of the scaling relationship, b1) ± 1 S.E. are given for each morphological variable. Isometric expectations for scaling coefficients are 1.0 for body dimensions and 2.0 for fin areas; only the caudal fin area deviates significantly from isometry.
F I G U R E 2 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 2 Representative kinematics for fast-starts in small (41 mm SL) and large (66 mm SL) adult mummichogs controlling for temperature. The top panel shows frames from high-speed videos at 10 ms intervals. To facilitate interpretation, original videos have been cropped to the same spatial dimensions and rotated to standardize starting orientation and escape direction (but note that digitization used unmodified videos). The graph in the bottom panel shows the angular displacement of the head (triangles) and the linear displacement of the fish center of mass (circles) for the trials depicted in the video frames, which were maximum performance trials for the small (filled shapes) and large (open shapes) fish tested at their acclimation temperature (24 C). The curve for head rotation was truncated at its maximum value to highlight the difference in time to form the initial C-bend. The curve for linear displacement continued until the end of stage 2 of the fast-start. Whereas small fish had higher rotation rates and executed the fast-start in less time, small and large fish moved their bodies over similar distances given equal amounts of time.
Transcription start site analysis for heterogenous CD4+ T cells using 5′ scRNA-seq
<p>These datasets are generated by ReapTEC (read-level pre-filtering and transcribed enhancer call) using 5' single-cell RNA-seq data on human heterogenous CD4+ T cells. By taking advantage of a unique "cap signature" derived from the 5′-end of a transcript, ReapTEC simultaneously profiles gene expression and enhancer activity at nucleotide resolution using 5′-end single-cell RNA-sequencing (5′ scRNA-seq). The detail of ReapTEC pipeline is described in https://github.com/MurakawaLab/ReapTEC.</p>
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
FIG. 1 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 1. — Map of Anatolia and the Levant with the main sites mentioned in the text (modified data courtesy of National Centers for Environmental Infor- mation – ETOPO1, Natural Earth and Geo Network opensource. https://doi. org/10.7289/V5C8276M).
FIG. 4 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 4. — Arslantepe, tarsometatarsi (left and right) of rooster from level IIIB. Photo credits: R. Ceccacci, ©MAIAO. Scale bar: 3 cm.
Deep decarbonisation for refineries starts in Ireland: introducing the REALISE pilot campaigns
<p>This webinar shared the cutting-edge research on CO<sub>2</sub> capture solvent stability and solvent management being carried out in a real-life refinery setting in order to support industry’s decarbonisation ambitions. The event introduced the scientists engaged in solvent testing campaigns at Irving Oil Whitegate Refinery in Ireland and SINTEF’s CO<sub>2</sub> laboratories at Tiller in Norway. It included a short film and a panel Q&A. </p> <p><strong>Programme</strong></p> <ul> <li>Welcome, introduction & REALISE CCUS project overview – Peter van Os, TNO</li> <li>Solvent testing in a real-life refinery setting: introduction – Juliana Monteiro, TNO</li> <li>Demonstration at Irving Oil Whitegate Refinery: video premiere and insights – Eirini Skylogianni, TNO</li> <li>Demonstration goals at SINTEF’s Tiller CO<sub>2</sub> labs – Thor Mejdell, SINTEF</li> <li>Panel Q&A</li> </ul>
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:50:45 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:34:33 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Dataset for Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda
<p>Dataset for the publication <strong>"Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda".</strong></p> <p>Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021</p> <p>Descriptive analysis of treatment-seeking pathways of and antimalarial treatment provision for children under 5 years with suspected severe malaria in three districts of Northern Uganda. All children first sought treatment from a community health worker before being referred to a higher-level facility.</p>
Weaving the Penelope Fabric. Part I: Starting Borders.
<p>A famous vase from Chiusi depicts Penelope in front of a patterned fabric. The video demonstrates how such a fabric could have been woven on a warp-weighted loom, and presents the technique of combining two starting borders in tablet-weaving. The footage stems from 2009 when Ellen Harlizius-Klück tried out the possibilities of a warp-weighted loom within an exhibition on ancient textile technology in the Clothiers Museum Bramsche (Germany).</p>
Text-fig. 13. a, d–g: Acer aemilianum. b, c: Acer palmatum and A. sieboldianum modern leaves (NMNS Cleared Leaf Database). a: Oriolo MSF 661, 7-lobed leaf. b: Specimen U 1049, lobe detail showing finely serrate leaf margin. c: Specimen T 0246, 9-lobed leaf with coarsely serrate leaf margin. d: Oriolo MSF 645-1, 9-lobed leaf with two small additional lobes. e: Oriolo MSF 645. f: Oriolo MSF 660, 9-lobed specimen. g: Oriolo MSF 660-1. White arrows indicate position along lamina lobes where marginal serration starts. Scale bars 10 mm (a–c, f, g), 50 mm (d, e). in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. 13. a, d–g: Acer aemilianum. b, c: Acer palmatum and A. sieboldianum modern leaves (NMNS Cleared Leaf Database). a: Oriolo MSF 661, 7-lobed leaf. b: Specimen U 1049, lobe detail showing finely serrate leaf margin. c: Specimen T 0246, 9-lobed leaf with coarsely serrate leaf margin. d: Oriolo MSF 645-1, 9-lobed leaf with two small additional lobes. e: Oriolo MSF 645. f: Oriolo MSF 660, 9-lobed specimen. g: Oriolo MSF 660-1. White arrows indicate position along lamina lobes where marginal serration starts. Scale bars 10 mm (a–c, f, g), 50 mm (d, e).
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180 in Fishes of War in the Pacific National Historic Park
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180° heading in the Asan Beach unit (NPS photo).
ScienceDex guides
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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.