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88 results for “CLARITY”
Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021
This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab
CLARITY busines canvas (draft)
<p>This draft CLARITY busines canvas is a result of the interactive session with potential users from the western Balkans region at the CLIMATEUROPE FESTIVAL 2018. It was used as input for designing the CLARITY busines canvas / busines models. </p>
Metrics As Scores Dataset: Price, Weight, and Other Properties of Over 1,200 Ideal-Cut and Best-Clarity Diamonds
<p>This dataset is a subset of the original diamonds dataset with more than 54,000 diamonds. It was reduced to only contain diamonds of the best cut (ideal) and clarity (IF). The group is now given by the colors from J (worst) to D (best). This dataset comes from the R-package ggplot2 (Wickham 2016). For each color, we can examine the following attributes (<strong>features</strong>) of each diamond:</p> <ul> <li><em>Carat</em>: Weight of the diamond</li> <li><em>Depth</em>: Total depth percentage</li> <li><em>Price</em>: Price in US dollars [discrete]</li> <li><em>Table</em>: Width of top of diamond relative to widest point</li> <li><em>X</em>: Length in mm</li> <li><em>Y</em>: Width in mm</li> <li><em>Z</em>: Depth in mm</li> </ul> <p>It has a total of 7 Colors (<strong>groups</strong>): <em>D</em>, <em>E</em>, <em>F</em>, <em>G</em>, <em>H</em>, <em>I</em>, and <em>J</em>. The best color is <em>D</em> and the worst color is <em>J</em>. This dataset was created to analyze whether there are differences between the colors.</p>
LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States
Time series of median summer water clarity (secchi) values from 601 unique lakes in the Midwest and Northeast United States. Water clarity observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1. These data were used to assess long-term changes in water clarity from 1987-2011, and the potential drivers of those trends (Lottig et al. in press). Summer open water period was used to approximate the stratified period in the study lakes, which was defined as June 15 to September 15. Over the 25-year time period, each lake had to have at least a single summer water clarity observation for 22 of 25 years. The median number of secchi measurements that were used to derive a single annual median value for each lake was approximately 9. Of the over 14,000 annual estimates of water clarity that we generated, only two percent of those annual values were generated from a single observation and median number of observations for each lake over the 25-year study period was 223. Each unique lake with water clarity data also has supporting geophysical data, including climate, land use, hydrology, and topography derived at multiple spatial scales. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03 except for the annual climate data which was aggregated at the HUC8 spatial scale from monthly PRISM data. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Citations: Lottig, N.R., P-N. Tan, T. Wager, K.S. Cheruvelil, P.A. Soranno, E.H. Stanley, C.E Scott, C.A. Stow, and S. Yuan. in press. Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., G
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Texas 2022 water clarity and color (FLAMe and Sentinel-2)
Water clarity and color were determined for six reservoirs using rapid spatial surveys from a sensor equipped boat and concurrent Sentinel-2 satellite imagery across Texas during drought conditions between the months of July and August 2022. From west to east, these systems include Red Bluff Reservoir, O.H. Ivie Lake, Lake Arrowhead, Lake Brownwood, Lake Waco, and Lake Bonham. For the water year leading up to the sampling dates, the precipitation ranged from 182 mm in Red Bluff Reservoir to 1036 mm in Lake Bonham. A total of 254 km of boat path were covered across the six reservoirs with a mean boat speed of 19.17 km/h. The data for this study covers three spatial approaches 1) along the boat path 2) longitudinal transects from dam to river arm and 3) whole system. For the boat path, data variables include turbidity measured continuously with a YSI EXO2 sonde, Secchi disk depth predicted from the turbidity values, normalized difference turbidity index (NDTI), and dominant wavelength. For both the longitudinal transects and whole system data, variables include the two remotely derived measures of clarity and color, NDTI and dominant wavelength. Data is also categorized by zone as either "arm" (reservoir arm) or "body" (main body) determined by a 4m depth threshold to compare between zones.
The Clarity Software Documentation Dataset
<p>This repository holds the Clarity Dataset which is a companion to the SANER'22 entitled "An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation". The dataset consists of 45,998 captions 10,204 GUI screenshots and xml metadata files (akin to the "html" for stipulating GUIs) of Android applications. The NL captions were obtained from human labelers, underwent several quality control mechanisms, and contain both high- (screen-level) and low-(component) level descriptions of screen functionality. This dataset is meant as a new source of data to augment techniques for software documentation that can take advantage of the rich pixel-based information contained within screenshots.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
Linked collectors and determiners for: A Gondwanan concept of Simplimorpha Scoble (sensu lato): a step toward clarity in the generic diagnostics of global Nepticulidae (Lepidoptera).
Natural history specimen data linked to collectors and determiners held within, "A Gondwanan concept of Simplimorpha Scoble (sensu lato): a step toward clarity in the generic diagnostics of global Nepticulidae (Lepidoptera)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3dad7d04-f7c6-4300-9240-8f2e44d0c257">https://bionomia.net/dataset/3dad7d04-f7c6-4300-9240-8f2e44d0c257</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3dad7d04-f7c6-4300-9240-8f2e44d0c257">https://gbif.org/dataset/3dad7d04-f7c6-4300-9240-8f2e44d0c257</a>. Formatted as a Frictionless Data package.
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Reporting Non-consensual Pornography: Clarity, Efficiency and Distress
<p>Experimental Data supporting the paper: Reporting Non-consensual Pornography:<br> Clarity, Efficiency and Distress</p>
Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"
<p>Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"</p>
The Aim of This Study is to Examine the Effect of Intravenously Administered Tranexamic Acid (TXA) on the Visual Clarity, Perioperative Hemorrhage, Duration and Early Postoperative Course of Shoulder
ClinicalTrials.gov study NCT05397652. IPD Sharing: YES. Countries: 1. Publications: 26.
CLARITY Extension Study
ClinicalTrials.gov study NCT00641537. IPD Sharing: Not stated. Countries: 31. Publications: 7.
Safety and Efficacy Study of the Neurent Medical NEUROMARK™ System in Subjects With Chronic Rhinitis (CLARITY)
ClinicalTrials.gov study NCT05324397. IPD Sharing: NO. Countries: 1. Publications: 2.
Study to Evaluate the Efficacy and Safety of Adjunctive Pimavanserin in Major Depressive Disorder (CLARITY)
ClinicalTrials.gov study NCT03018340. IPD Sharing: NO. Countries: 1. Publications: 2.
In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors
Open the record for dataset details and reuse information.
Data from: Finding clarity in ecological outcomes using empirical integrated social-ecological systems: a case study of agriculture-dependent grassland birds
<p>1. Efforts to monitor and conserve populations and ecosystems in human-dominated landscapes can benefit from an empirical social-ecological systems approach.<br> 2. Here we illustrate how latent variable structural equation modelling of regional time series data can effectively describe interconnected drivers of population fluctuations in dynamic landscapes and can help to reveal previously unknown system drivers.<br> 3. Using a declining farmland-dependent bird species (Ammodramus savannarum) in the eastern United States (1994-2015) as a case study, our analysis reveals how farm management decisions drive population fluctuations (R<sup>2</sup> = 20%), while management is in turn highly influenced by climate (R<sup>2</sup> = 23-51%), but not by regional conservation spending.<br> 4. Synthesis and applications. Structural equation modelling revealed potential social-ecological pathways for halting regional population declines in a grassland bird, the Grasshopper Sparrow. Lower population growth rates followed years of higher hay yields (~4 percentage points per metric ton increase in hay yield) and later harvests (~2 percentage points per 10-day delay in harvest). Thus, one pathway for stabilising regional populations could involve compensating farmers for reducing hay harvests, potentially requiring a six-fold increase in current annual agri-environmental conservation spending.</p>
Licence to share: how Creative Commons brought clarity to the digital rights and enabled fair content reuse across the web.
<p>CC 0, CC BY or CC BY-NC-SA? We talked to Cable Green, the interim CEO and Director of Open Education at Creative Commons. Cable explained the rationale behind the licenses on digital work and provided us with a practical guide to how to use these. Further on, we ventured into the world of Open Education and how it is empowered by Creative Commons licensing. And yes - we did also talk about Covid-19 and how it is changing the way we think about open access and collaboration.</p> <p>Episode links:</p> <p><a href="https://creativecommons.org/">https://creativecommons.org/</a></p>
Timbral Clarity Dumping Bias Dataset
<p>Data generated as part of research to determine the influence of 'dumping bias' on listener ratings of timbral clarity. Data comprise audio files, listening test interfaces, results and MATLAB code for plot generation.</p> <p><strong>References</strong></p> <p>AES 139 (2015): Hermes, K., Brookes, T., Hummersone, C., “The influence of dumping bias on timbral clarity ratings”, 139th Audio Engineering Society Convention, New York, USA, November 2015.</p>
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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.