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10,553 results for “measurements”
Fig. 1 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 1. The map of Hungary with the position of the sampling sites (filled squares show light traps)
Fig. 3 in A New Measure Of Conservation Value Combining Rarity And Ecological Diversity: A Case Study With Light Trap Collected Caddisflies (Insecta: Trichoptera)
Fig. 3. The diversity (A) and RAR-index (B) of the different aquatic habitats (aquatic habitats with the same letter are not significantly different at p = 0.05 by non-parametric Tukey-test)
Fig. 3 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 3. Correlation between the body mass of caught fat dormice (Glis glis L.) and the days of hunting for the locality of Vrbovsko during the autumn of 1994
Fig. 6 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 6. Correlation between the number of caught fat dormice (Glis glis L.) and the days in the hunting season for the locality of Gerovo during the autumn of 1999
Fig. 5 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 5. Correlation between the number of caught fat dormice (Glis glis L.) and the days in the hunting season for the locality of Vrbovsko during the autumn of 1994
Fig. 4 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 4. Correlation between the body mass of caught fat dormice (Glis glis L.) and the days of hunting for the locality of Gerovo during the autumn of 1999
Fig. 1 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 1. The area of protection, permitted hunting and research of the fat dormouse (Glis glis L.) in Croatia
Fig. 2 in Body Measurements And Harversting Dynamics Of The Fat Dormouse (Glis Glis L.) In The Mountainous Part Of Croatia
Fig. 2. Trends in harvesting the fat dormouse (Glis glis L.) in the municipalities of Gerovo (1), Tršće (2) and Prezid (3) between 1991 and 2001
Fig. 5 in Distinguishing Mus Spicilegus From Mus Musculus (Rodentia, Muridae) By Using Cranial Measurements
Fig. 5. The hyperbolic regression of coefficient of variation (CV) on mean (X). The regression of the joint exponential grow curve: r = 0.86 (proportion of variance accounted for: 0.73).
Transfer function measurements for simulating environmental noise at hearable microphones
<p>This dataset is supplementary material to the conference paper "Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments" presented at ICASSP 2024 [1].</p> <p>The dataset consists of impulse response measurements for 18 device users (5 female, 13 male) wearing hearable devices in both ears. <br>The dataset was recorded in a sound-proof listening room using the Hearpiece prototype device (closed vent variant) [2] with a sampling frequency of 44.1 kHz.<br>Impulse responses were measured with exponential sweeps from 80 Hz to 22.05 kHz with a duration of 3s played from 8 loudspeakers arranged in a circle of approximately 1.5m radius. <br>The loudspeakers were located in the horizontal plane around the device users in 45°-steps (azimuth), starting from 22.5° to the right (where 0° is the front from the device users' perspective).</p> <p>The measurements are contained in the folder <code>measurements</code>. Each subfolder contains measurements from a different device user (e.g., <code>VP_01</code>). <br>Each file contains the measurement for one direction, e.g. <code>VP_01/data_0.npz</code> contains the measurement of device user <code>VP_01</code> for 22.5° azimuth, <code>VP_01/data_1.npz</code> is the measurement for the same device user for 22.5°+45° and so on.<br>Measurements of device users where the device could not be inserted, or where the fit did not provide sufficient attenuation of external sounds to the in-ear microphone, were excluded.</p> <p>The impulse responses for two Hearpiece devices (closed vent), the concha and in-ear microphones were measured.<br>A DPA 6060 lavalier clip microphone and a Tbone SC140 cardiod microphone were also included in the measurement as reference channels. </p> <p>The channels of the measurements (counting from 0):</p> <p> 0: Lavalier-microphone clipped to the shirt neck, shirt collar etc. of the device user<br> 1: Reference microphone about 50 cm in front of the device user<br> 2: Left in-ear microphone Hearpiece<br> 3: Left concha microphone Hearpiece<br> 4: Right in-ear microphone Hearpiece<br> 5: Right concha microphone Hearpiece</p> <p><br>The measurement consists of impulse responses from the loudspeaker to the hearable device microphones and reference microphones, and corresponding transfer functions. <br>Measurement metadata is included as well. <br>The measurement files contain a python dictionary with the following fields:</p> <ul> <li><code>test_signal</code>: the signal used for playback, consisting of a pause, the sweep, and another pause</li> <li><code>rec_signal</code>: the recorded signal (sweep played from the loudspeaker, recorded at the microphones)</li> <li><code>sweep</code>: the generated exponential sweep signal without pauses</li> <li><code>T</code>: actual duration of the sweep (~2 Seconds)</li> <li><code>sweep_inv</code>: inverse sweep (inverse w.r.t convolution of the sweep with the system response)</li> <li><code>sweep_inv_spectrum</code>: spectrum of the inverse sweep</li> <li><code>f11</code>: the frequency (in Hz) corresponding to the <code>RampLen</code> of the fade-in at the beginning of the sweep</li> <li><code>T_desd</code>: desired duration of the sweep in seconds (2 Seconds)</li> <li><code>T_rec</code>: recording duration in seconds (3 Seconds)</li> <li><code>start_frequency</code>: Minimum frequency in the measurement / first frequency in the sweep (80 Hz)</li> <li><code>RampLen</code>: Length of the fade-in ramp applied to the beginning of the sweep (based on a Hanning window) (2048 Samples)</li> <li><code>pre_pause_len</code>: pause time between starting the measurement and sweep playback (88200 Samples)</li> <li><code>after_pause_len</code>: pause time after sweep playback (44100 Samples)</li> <li><code>n_repetitions</code>: Number of repetitions for the measurement (1)</li> <li><code>n_channels</code>: Number of recorded channels including loopback (7 = 4 Hearpiece, 2 reference, 1 loopback)</li> <li><code>coh_mat</code>: Mean Squared Coherence per channel (between the measured sweep and the playback sweep signal), has shape (frequencies up to <code>samplerate</code>/2 x channels)</li> <li><code>ir_loopback</code>: the measured impulse response of the loopback channel, used to measure and compensate system delay from audio interface</li> <li><code>ir_mic</code>: the measured impulse responses of the hearable and reference microphones, with shape (samples, channels)</li> <li><code>tf_mic</code>: the measured transfer functions between the loudspeaker and the hearable and reference microphones, with shape (frequencies up to <code>samplerate</code>/2, channels)</li> <li><code>system_delay</code>: the measured system delay from the audio interface (position of the peak of the correlation between playback sweep and loopback sweep signals)</li> <li><code>samplerate</code>: The sampling rate used for the measurements (44100 Hz)</li> </ul> <p>This dataset is compatible with the German own voice recordings available at <a href="../records/10844599" target="_blank" rel="noopener">https://zenodo.org/records/10844599</a> (same participants+device insertion and measurement setup).</p> <p>The example script <code>generate_indiv_noise_dataset.py</code> can be used to augment a single-channel noise dataset to obtain simulated individual hearable noise signals,<br>similar to [1] but using impulse responses directly as filters instead of first computing relative transfer functions and then applying them in the STFT domain.</p> <p><br>[1] M. Ohlenbusch, C. Rollwage, S. Doclo: "Multi-microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments". In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, South Korea, Apr. 2024, pp. 416-420.<br>[2] F. Denk, M. Lettau, H. Schepker, S. Doclo, R. Roden, M. Blau, J.-H. Bach, J. Wellmann, and B. Kollmeier: "A One-Size-Fits-All Earpiece with Multiple Microphones and Drivers for Hearing Device Research". In: Proc. AES International Conference on Headphone Technology. San Francisco, USA, Aug. 2019.</p>
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
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Redox potential and related ancillary measurements from Lakkasuo raised mire in 2014-2016
<p>The dataset contains redox potential and related ancillary measurements from the Lakkasuo raised mire complex from 2014 to 2016. </p>
FIGURE 1. Linear measurements used for morphological comparison. B-L in Fossil moles from the Gray Fossil Site (Tennessee): Implications for diversification and evolution of North American Talpidae
FIGURE 1. Linear measurements used for morphological comparison. B-L - buccolingual width; upper teeth - posterior-most tip of the metastyle to the lingual margin of the protocone; lower teeth - lingual-most tip of the metaconid to the buccal margin of the talonid. A-P - anteroposterior length; upper teeth - posterior-most tip of the metastyle to the anterior-most tip of the parastyle; lower teeth - anterior tip of the trigonid (paraconid) to the posterior end of the talonid (entostylid). TRI - width of the trigonid. TAL - width of the taloned basin. HL - total humerus length; proximal-most tip of greater tuberosity to distal-most point on capitulum. PW - width of the proximal end of the humerus: lateral most extent of the lesser tuberosity to the medial most aspect of the greater tuberosity. GT - length of the greater tuberosity. LT - length of the lesser tuberosity. DW - width of the distal end of the humerus: lateral most extent (entepicondylar process) to the medial most extent (ectepicondylar process). RL - total radius length: proximal-most tip of capitular process to distal-most tip of lunar articular facet. DL - length of the diaphysis. UF - length of the lunar articular facet. TUL - total ulna length; proximal-most tip of olecranon process to distal-most tip of terminal process. UL - ulna length without the olecranon fossa. Elements not to scale.
A Preisach method for estimating absolute paleofield intensity under the constraint of using only isothermal measurements: 2. Experimental testing [DATASET]
<p>Raw FORC data published in:</p> <p>Muxworthy, A.R., Heslop, D., Paterson, G.A., Michalk, D., 2011. A Preisach method for estimating absolute paleofield intensity under the constraint of using only isothermal measurements: 2. Experimental testing. J. Geophys. Res. 116, B04103, doi:04110.01029/02010JB007844.</p>
Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi
<p>Premise of the study While many studies have measured the aboveground responses of plants to mycorrhizal fungi at a single time point, little is known about how plants respond belowground or across time to mycorrhizal symbiosis. By measuring belowground responses as well as growth over time in many plant species, we create a more complete picture of how mycorrhizal fungi benefit their hosts. Methods We grew 26 prairie plant species with and without mycorrhizal fungi and measured fourteen functional traits measuring above and belowground tissue quality and quantity responses and changes in resource allocation. We used function-value trait (FVT) modeling to characterize changes in species growth rate when colonized. Key results While aboveground biomass responses were positive, the response of traits belowground were much more variable. Changes in aboveground biomass accounted for 60.8% of the variation in mycorrhizal responses, supporting the use of aboveground biomass response as the primary response trait. Responses belowground were not associated with aboveground responses and accounted for 18.3% of the variation. Growth responses over time were highly variable across species. Interestingly, none of the measured responses were phylogenetically conserved. Conclusions Mycorrhizal fungi increase plant growth in most scenarios, but the effects of these fungi belowground and across time are more complicated. This study highlights how differences in plant allocation priorities might affect how they utilize the benefits from mycorrhizal fungi. Identifying and characterizing these differences is a key step to understanding the effects of mycorrhizal mutualisms on whole plant physiology. </p>
Figure 2 in Measuring the scientific impact of FishBase after three decades
Figure 2. – Four indicators for the impact of FishBase. A: Annual number of 'hits' that the FishBase webpage has been receiving since 1998. Data derived from https://www.fishbase.se/WebUse.php?yr=All. *'hits' are defined as all requests made to the server and are used as a representation of FishBase usage. The number of substantial user sessions is by default considerably lower, i.e., around 1 million per month in 2020. B:Annual number of citations to FishBase, based on data from Scopus (www.scopus.com, accessed on August 16, 2021). C: Annual number of citations to FishBase, based on data from Google Scholar (www.scholar.google.com, accessed on January 13, 2021). D: Occurrence in famons (1 famon = 10-6 relative % Ngram frequency: Stergiou, 2017) of the term "fishbase" (case insensitive) in the corpora of English (American and British), French, and Spanish books published between 1994 and 2018, based on data from the Google Books Ngram Corpus (http://books.google.com/ngrams; accessed on October 2, 2021).
Figure 4 in Measuring the scientific impact of FishBase after three decades
Figure 4. – Subject areas and context of FishBase citations. A: Number of documents citing FishBase by subject area, based on data from Scopus (www.scopus.com, accessed on August 16, 2021). Note that documents may fall into more than one category. B: Context in which the 107 most highly cited papers (1% of the total FishBase citations from 1994 to 2020, based on Scopus) reference FishBase (neutral/ technical, positive, negative) and C: How they use it (as a source of data or a simple reference to it).
Figure 3 in Measuring the scientific impact of FishBase after three decades
Figure 3. – Bubble map visualization of the global distribution of FishBase citations for the period 1994-2020, based on data from Scopus (www.scopus.com, accessed on August 16, 2021). Bubble size is relative to the number of documents citing FishBase (maximum number: 2,022 documents produced by the United States of America).
FIGURE A5 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A5. Scatter plot displaying how beam shift, measured by the apparent growth (scale) of the shell in the final x-ray radiograph relative to the first x-ray radiograph, varies with (1) time of day the scan was performed, and (2) the overall scan time. We found that beam shift decreased throughout the day, likely due to the tube warming up as more scans are run. There was no correlation between overall scan time and the amount of beam shift.
FIGURE A1.2 in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data
FIGURE A1.2. The range of volume measurements for each exposure times when three scans were reconstructed using ten methods. The blue dot represents the simple surface determination used for all shells in this study. The error bars represent the data range for each exposure time.
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.