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4,725 results for “Normalization”
Alaska Statewide annual maximum Normalized Difference Vegetation Index (NDVI) values from 1982-2003 at 8km pixel size
Maximum annual NDVI values derived from twice monthly GIMMS-NDVI data, acquired for the period of 1982-2003 were downloaded fry the University of Maryland Global Land Cover Facility (http://www.landcover.org). These data were originally maximum NDVI values for each 64km2 pixel for each 15-day composite period. By selecting the maximum NDVI during each 15-day period, non-vegetation effects such as cloud or smoke contamination, and view geometry effects are reduced. The data had been calibrated by NASA scientists to correct for orbital drift and sensor degradation from a time series of five NOAA AVHRR sensors. The data were also processed by NASA scientists to correct for atmospheric effects resulting from two major volcanic eruptions: El Chichon in 1982, Mt. Pinatubo in 1991.
McMurdo Dry Valleys LTER: Microbial mat biomass and Normalized Difference Vegetation Index (NDVI) values from Lake Fryxell Basin, Antarctica, January 2018
This package contains data collected from microbial mat surveys (i.e., percent cover, ash-free dry mass (AFDM), and pigment concentrations – chlorophyll-a, scytonemin, and carotenoids) associated with satellite-derived Normalized Difference Vegetation Index (NDVI) values from the Lake Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys of Antarctica. The purpose of this study was to quantitatively compare key microbial mat characteristics to NDVI. Data were collected at seven plot locations within the Canada Glacier Antarctic Specially Protected Area (ASPA) near Canada Stream, as well as alongside Green Creek and McKnight Creek. NDVI values were derived from a WorldView-2 multispectral satellite image taken of the Lake Fryxell Basin on January 19, 2018, while biological ground surveying and sampling were conducted during the 2nd and 4th weeks of January 2018.
Selective auditory attention in normal-hearing and hearing-impaired listeners
<p>This repository contains the EEG and behavioral data described in:</p> <p>Fuglsang, S A, Märcher-Rørsted, J, Dau, T, Hjortkjær, J (2020). Effects of sensorineural hearing loss on cortical synchronization to competing speech during selective attention. Journal of Neuroscience, 40(12):2562–2572, <a href="https://doi.org/10.1523/JNEUROSCI.1936-19.2020">https://doi.org/10.1523/JNEUROSCI.1936-19.2020</a> </p> <p>Please cite this paper when using the data</p> <p>The data set consists of response data for 22 hearing-impaired and 22 normal-hearing participants. It includes:<br> - EEG data: responses to two-talker and single-talker speech stimuli<br> - Envelopes of the corresponding speech audio<br> - EEG data: responses to 1 kHz tone beeps for ERPs<br> - EEG data: responses to periodic tone sequences for Envelope-following responses (EFRs)<br> - EEG resting-state data recorded with eyes-open and eyes-closed<br> - inEar EEG data for 19 of the 44 subjects (EEG recorded inside the ear canals)<br> - Behavioral data: speech comprehension scores, task difficulty ratings, speech-in-noise scores (SRTs), tone-in-noise scores, digit span working memory scores, SSQ questionnaire ratings<br> - Pure-tone audiograms</p> <p>For more information, see the README and 'dataset_description.json' file.</p> <p><br> Format<br> ------<br> The dataset is formatted according to BIDS version 1.3.0 and the BIDS standard extension for EEG (BEP006) that has been merged in the main body of the specification. For more details, see https://bids-specification.readthedocs.io/en/latest/06-extensions.html</p> <p>Behavioural data are stored in the 'participants.tsv' file. Task-difficulty ratings and multiple choice questionnaire data from the selective attention experiment are stored in the events files (see 'task-selectiveattention_events.json'). </p> <p> </p> <p>Code<br> ------<br> Code for analyzing the data is available at: https://gitlab.com/sfugl/snhl</p> <p> </p> <p>Audio<br> ------<br> Envelopes of the audio signals are included in the data set. For inquiries regarding the raw audio data, please send an email to jensh@drcmr.dk with the subject line "ds-eeg-snhl audio".</p> <p> </p> <p>Acknowledgments<br> ----------<br> This work was supported by the EU H2020-ICT grant number 644732 (COCOHA: Cognitive Control of a Hearing Aid) and by the Novo Nordisk Foundation synergy grant NNF17OC0027872 (UHeal). The EarEEG were kindly provided by Eriksholm Research Centre.</p>
supplementary of UTUC-Normal 2
<p>Accompanies a manuscript regarding the mutant clonal expansion in human morphologically normal urothelium (bladder and ureter). Contains: </p> <p>1. Raw input for the Sequenza analysis (large datasets).</p>
Affymetrix Normalization Required Files in GIANT tool suite
<p>This archive contains affymetrix files necessary to normalize microarrays data and modified annotations files required in GIANT APT-Normalize tool for annotation of normalized data.</p>
Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.
<p>Supplementary Material Chapter 1.</p> <p> </p> <p>Table # 3: Output of Tax4Fun of predicted functional profiles. KO IDs and a KO abundance in every samples after a process of normalization.</p>
Physics in Precision-Dependent Normal Neighborhoods
<p>This dataset is associated with</p> <p>"Physics in Precision-Dependent Normal Neighborhoods",<br> Bruno Hoegl, Stefan Hofmann, and Maximilian Koegler<br> (2020) [https://arxiv.org/abs/2007.15717]</p> <p>It includes mathematica 11.0.1.0 notebooks to allow for the replication of figures 2 and 3 as well as equations (26), (29), and (31).</p>
Data from: Evidence for normal novel object recognition abilities in developmental prosopagnosia
<p>The issue of the face specificity of recognition deficits in developmental prosopagnosia (DP) is fundamental to the organisation of high-level visual memory and has been increasingly debated in recent years. Previous DP investigations have found some evidence of object recognition impairments, but have almost exclusively used familiar objects (e.g., cars), where performance may depend on acquired object-specific experience and related visual expertise. An object recognition test not influenced by experience could provide a better, less contaminated measure of DPs' object recognition abilities. To investigate this, in the current study we tested 30 DPs and 30 matched controls on a novel object memory test (NOMT Ziggerins) and the Cambridge Face Memory Test (CFMT). DPs were impaired on the CFMT but showed no differences in accuracy or reaction times to controls on the NOMT. We found similar results when comparing DPs to a larger sample of 274 web-based controls. Additional individual analyses demonstrated that the rates of object recognition impairment in DPs did not differ from the rate of impairment in either control group. Together, these results demonstrate unimpaired object recognition in DPs for a class of novel objects that serves as a powerful index for broader novel object recognition capacity.</p>
Displacement accumulation and sampling of paleoearthquakes on active normal faults of Crete in the eastern Mediterranean
<p>The Table S1 (uploaded separately) presents fault data and calculated values of earthquake parameters that underpin the interpretations and conclusions presented in Nicol et al. (G3, submitted June 20, 2020). The Locations of each fault are shown in Figure 2 of the main manuscript.</p> <p>In detail: The Table S1 summarises the attributes on all active faults studied on Crete. Single Event Displacement (SED), Recurrence Interval (RI), Moment Magnitude (M<sub>w</sub>) and number of paeoearthquakes have been estimated for each fault using topographic fault lengths and Quaternary displacement rates in conjunction with the Wesnousky (2008) equations (see table footnote for equations used to calculate SED and Mw). Mw calculated using Wesnously (2008) equations are unconstrained at lengths < 15.5 km. Note that the short-term (e.g. the post-glacial period) displacement rates for the Lentas (ID=43) and South Central Crete (ID=44) faults derive from time periods of c. 50 kyr and 125 kyr, respectively (from Gallen et al., 2014). Nevertheless, the estimated number of events on these two faults correspond to the post-glacial period (16.5 kyr). Table S1 presents uncertainties of +40% and -20% for maximum displacements derived from topography. Here we are conservative and have allowed larger errors for the maximum fault displacements to account for greater errors associated with erosion. The calculated earthquake recurrence interval (RI) in Table S1 derives from SED/Quaternary displacement rate.</p>
The 2009 Mw6.1 L'Aquila normal fault system imaged by 64,051 high-precision foreshock and aftershock locations.
<p>The earthquake catalogue is composed by 64,051 high-precision foreshock and aftershock recorded during the Mw6.1 2009 L'Aquila (Central Italy) normal faulting seismic sequence. The catalog includes events occurred between 1<sup>st</sup> of January and 31<sup>st</sup> December 2009. The completeness magnitude is 0.7. Earthquake locations were obtained by combining an automatic picking procedure for P and S waves, together with cross-correlation and double-difference location methods. </p> <p>Seismic data were recorded at a very dense local network composed of 67 three-component seismic stations (20 permanent stations of the Italian National Network located within 80 km from the epicentral area and 47 temporary stations installed soon after the occurrence of the main shock [Margheriti et al., 2011]). </p> <p>Earthquakes were extracted by the continuous recordings by applying a detection algorithm to all stations, based on the classical STA/LTA coincidence-sum algorithm applied to the trace of the 3C covariance matrix. To these events, we applied an automatic picking algorithm (Manneken Pix) [Di Stefano et al., 2006] able to provide about 1.9 million P-wave and 503,000 S-wave accurate readings, with an estimation of the measurement errors. </p> <p>We applied a time domain cross-correlation method (Schaff and Waldhauser, 2005) to all event pairs with separation distances ≤ 5 km at all stations that recorded the pair. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. We selected measurements with correlation coefficients greater than 0.85, resulting in a total of ~190 million P and ~85 million S-wave delay times. </p> <p>Earthquakes were located following a two steps procedure. Initial locations for 133,236 events were computed with the Hypoellipse code [Lahr , 1989] using a 1D P-wave gradient velocity model optimized for the area [Chiaraluce et al., 2011]. In the second step, we computed relative locations by applying the large scale double-difference method described in Waldhauser and Schaff, (2008) to the catalog picks and phase delay times measured from waveform cross correlation. The entire dataset was sub-divided in 84 rectangular overlapping boxes, containing a maximum of 3000 earthquakes, orthogonal to the mean strike of the seismic sequence. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008). </p> <p>The final double-difference catalog includes 64,051 events. A subset made of 51,271 earthquakes (i.e., 80% of the whole dataset) indicates highly correlated earthquakes, having at least 10 P-waves and 5 S-waves correlated phases with at least one other event. Highly correlated events (flag=1 in the attached file) mostly occur on the major fault segments, while poorly correlated earthquakes (flag=0 in the attached file) mostly occur in the volume around the major faults.</p> <p>The attached file is a plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>id_dd: </strong>the hypoDD unique event identifier</p> <p><strong>origin_time: </strong>date of the origin time in the format YYYY-MM-DD[T]hh:mm:ss.msec</p> <p><strong>lat</strong>: hypocenter latitude expressed in degrees </p> <p><strong>lon</strong>: hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep</strong>: hypocenter depth expressed in km </p> <p><strong>mag</strong>: magnitude (pure number)</p> <p><strong>flag</strong>:<strong> </strong>1 for highly correlated earthquakes; 0 for poorly correlated earthquakes. </p> <p> </p> <p> </p> <p> </p>
Data of normal spectral emissivity measurements for Ta, Mo, W and Nb
<p>Data aquired during my master thesis about the normal spectral emissivty of Ta, Mo, W and Nb measured with an ohmic pulse heating apparatus and a us-DOAP</p>
Data from: Characterization of gut microbiota composition in hemodialysis patients with normal weight obesity
<p><b>Background:</b> Normal weight obesity (NWO), defined by a normal body mass index (BMI) but increased body fat percentage (BF%), is associated with an increased risk of cardiovascular disease and mortality. NWO is characterized by inflammation and muscle wasting in chronic kidney disease (CKD), but the underlying mechanisms remain largely unknown. Gut microbiota has been implicated in the regulation of host metabolism and may play important roles in the development of NWO in CKD.</p> <p><b>Methods:</b> In this case-control study, we examined the gut microbial diversity and taxonomy in 96 hemodialysis patients with normal weight (BMI <25 kg/m<sup>2</sup> and BF% ≤25% for men or ≤35% for women, n = 32), NWO (BMI <25 kg/m<sup>2</sup> and BF% >25% for men or >35% for women, n = 32), and overweight/obesity (BMI ≥25 kg/m<sup>2</sup>, n = 32), matched for age, gender, and diabetes. BF% was measured using bioimpedance spectroscopy device. Gut microbiota was determined by 16S rRNA sequencing.</p> <p><b>Results:</b> We found that α-diversity was significantly different among the 3 adiposity phenotypes, with NWO being the least diverse. α-diversity was positively correlated with BMI, subjective global assessment score, and physical activity, but negatively correlated with interleukin-6 and tumor necrosis factor-α. Patients with or without NWO were distinguished with respect to principal coordinate analysis of β-diversity. Notably, the relative abundance of butyrate-producing bacteria, such as <i>Faecalibacterium </i><i>prausnitzii</i> and<i> Coprococcus</i>, was markedly reduced in patients with NWO.</p> <p><b>Conclusion:</b> Our findings support associations between gut dysbiosis and a proinflammatory and catabolic state in hemodialysis patients with NWO.</p>
Data from: The Chord-Normalized Expected Species Shared (CNESS)-distance represents a superior measure of species turnover patterns
<p>1. Measures of β-diversity characterizing the difference in species composition between samples are commonly used in ecological studies. Nonetheless, commonly used dissimilarity measures require high sample completeness, or at least similar sample sizes between samples. In contrast, the Chord-Normalized Expected Species Shared (CNESS) dissimilarity measure calculates the probability of collecting the same set of species in random samples of a standardized size, and hence is not sensitive to completeness or size of compared samples. To date, this index has enjoyed limited use due to difficulties in its calculation and scarcity of studies systematically comparing it with other measures.</p> <p>2. Here, we developed a novel R function that enables users to calculate ESS (Expected Species Shared)-associated measures. We evaluate the performance of the CNESS index based on simulated datasets of known species distribution structure, and compared CNESS with more widespread dissimilarity measures (Bray-Curtis index, Chao-Sørensen index, and proportionality based Euclidean distances) for varying sample completeness and sample sizes.</p> <p>3. Simulation results indicated that for small sample size (m) values, CNESS chiefly reflects similarities in dominant species, while selecting large m values emphasizes differences in the overall species assemblages. Permutation tests revealed that CNESS has a consistently low CV (coefficient of variation) even where sample completeness varies, while the Chao-Sørensen index has a high CV particularly for low sampling completeness. CNESS distances are also more robust than other indices with regards to undersampling, particularly when chiefly rare species are shared between two assemblages.</p> <p>4. Our results emphasize the superiority of CNESS for comparisons of samples diverging in sample completeness and size, which is particularly important in studies of highly mobile and species-rich taxa where sample completeness is often low. Via changes in the sample size parameter m, CNESS furthermore cannot only provide insights into the similarity of the overall distribution structure of shared species, but also into the differences in dominant and rare species, hence allowing additional, valuable insights beyond the capability of more widespread measures.<br> </p>
Data from: Rethinking 'normal': the role of stochasticity in the phenology of a synchronously breeding seabird
1. Phenological changes have been observed in a variety of systems over the past century. There is concern that, as a consequence, ecological interactions are becoming increasingly mismatched in time, with negative consequences for ecological function. 2. Significant spatial heterogeneity (inter-site) and temporal variability (inter-annual) can make it difficult to separate intrinsic, extrinsic, and stochastic drivers of phenological variability. The goal of this study was to understand the timing and variability of breeding phenology of Adélie penguins under fixed environmental conditions, and to use those data to identify a 'null model' appropriate for disentangling the sources of variation in wild populations. 3. Data on clutch initiation were collected from both wild and captive populations of Adélie penguins. Clutch initiation in the captive population was modeled as a function of year, individual, and age to better understand phenological patterns observed in the wild population. 4. Captive populations displayed as much inter-annual variability in breeding phenology as wild populations, suggesting that variability in breeding phenology is the norm and thus may be an unreliable indicator of environmental forcing. The distribution of clutch initiation dates was found to be moderately asymmetric (right skewed) both in the wild and in captivity, consistent with the pattern expected under social facilitation. 5. The role of stochasticity in phenological processes has heretofore been largely ignored. However, these results suggest that inter-annual variability in breeding phenology can arise independent of any environmental or demographic drivers and that synchronous breeding can enhance inherent stochasticity. This complicates efforts to relate phenological variation to environmental variability in the wild. Accordingly, we must be careful to consider random forcing in phenological processes, lest we fit models to data dominated by random noise. This is particularly true for colonial species where breeding synchrony may outweigh each individual's effort to time breeding with optimal environmental conditions. Our study highlights the importance of identifying appropriate null models for studying phenology.
Google Trends normalized hits for COVID-19 generated in Switzerland
<p>This dataset contains the normalized hits of the queries on COVID-19 generated in Switzerland between July 2019 and July 2020, the timeline of the normalized hits per Canton, the top associated queries, the frequency analysis of the top associated queries, the categorization of the top associated queries, and the rising queries. </p> <p>Query: Coronavirus + covid + 2019-nCoV + SARS-CoV2</p> <p>Query type: keyword</p> <p>Timeframe: 01/01/2020 - 11/06/2021</p> <p>Date of search: 22/06/2021</p> <p>Data source: Web searches</p> <p>Location:CH (by canton)</p> <p>Query category:all</p> <p>Rationale for keywords: main semi-synonims for covid-19</p> <p>This dataset has been generated by and used for the <a href="https://www.ibme.uzh.ch/en/Biomedical-Ethics/Research/Ongoing-Research/Public-Health-Ethics/PubliCo.html">PubliCo research project</a>.</p>
Image tiles of TCGA-CRC-DX histological whole slide images, non-normalized, tumor only
<p>These are image tiles of tumor tissue of N=604 colorectal cancer (CRC) histological whole slide images in the TCGA database. The tumor tissue was manually outlined and cut into non-overlapping tiles of 512x512 px at 0.5 µm/px. No further preprocessing was applied. No color normalization was applied. Tiles are compressed with JPEG. Each ZIP file corresponds to one whole slide image (original format: SVS).</p> <p>Please cite our previous publication related to this dataset: https://www.nature.com/articles/s41591-019-0462-y</p> <p>Please observe the original TCGA licenses if you use the data https://portal.gdc.cancer.gov/</p> <p>Original image credit to TCGA: https://portal.gdc.cancer.gov/</p> <p>Image tiles were created with QuPath v0.1.2 https://qupath.github.io</p> <p>Genetic data matching these images are available at https://cbioportal.org</p> <p>More information on the procedures: https://zenodo.org/record/3694994</p>
Wood Normal Map RTI
Using RTI to generate the diffuse and normal maps, I was able to make them into a 2.5D relit texture. I wish I had the ability to create specular and diffuse maps, but I don't think I'd be able to write programs to do that for me. Thanks to CHI and Leszek Pawlowicz for the amazing documentation and programs. Scans were done by AISOS, a part of LATIS Labs in the College of Liberal Arts at the University of Minnesota, Twin Cities. Source: Objaverse 1.0 / Sketchfab
Aged Brick Wall with Normal Map Photogrammetry
Corner of Coglin and Second Street in Brompton McLeays Carpet Bulk store Source: Objaverse 1.0 / Sketchfab
Quarter Normal Map RTI
Finally a test of the newly completed small RTI dome, with 48 LEDs instead of the 64 of the big dome. Have a look at that sweet matcap! Scans were done by AISOS, a part of LATIS Labs in the College of Liberal Arts at the University of Minnesota, Twin Cities. Source: Objaverse 1.0 / Sketchfab
An MRI DICOM data set of the head of a normal male human aged 52
<p>This zip file contains a DICOM data set of magnetic resonance images a normal male mathematics professor aged 52. The experimental subject is the author. The MRI scans are T2 weighted turbo-spin-echo (T2W TSE) and T1 weighted Fast Field Echo (T1W FFE).</p> <p>The subject suffers from a small vertical strabismus (hypertropia), a misalignment of the eyes, which is visible in this data set.</p> <p>The author would like to thank the Radiology Department at the Macclesfield General Hospital for performing the scan.</p> <p> </p>
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.