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833 results for “Consistency”
STIR ROOT Consistency Test Data
<p>Data generated by `ROOT_STIR_consistency` for the `test_view_offset_root` test. This data is pre-generated GATE data of point sources, measured by a GATE geometry (similar to the GE Discovery 690), that is to be used to test the allignment between STIR and GATE crystal positions.</p>
Consistent Bondi SPH for frontiers 2022
<p>Data to be combined with the notebook scripts: https://github.com/josemramirez/poly_frontiers2022, to reproduce all the results of the paper.</p> <p>4 sets for 4 different values of gamma = G1 = 1; G1_1 = 1.1; G3_2 = 1.5; G5_3 = 1.66.</p> <p>(1) Unzip in the same directory of the scripts.</p> <p>ps: last version v3.0 computed with the last version of Qs parameters = BH_v8.0e.</p>
The dataset by "Spectrally Consistent Scattering, Absorption, and Polarization Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 μm"
<p>This is the ice crystal single-scattering property dataset introduced in the paper "Yang, Ping, et al. "Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 to 100 μ m." <em>Journal of the Atmospheric Sciences</em> 70.1 (2013): 330-347.".</p>
Dataset: Consistent release of volatile organic compounds across an actively degrading permafrost peatland
<p>Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds.</p> <p>This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication <a href="https://doi.org/10.1016/j.geoderma.2023.116355">https://doi.org/10.1016/j.geoderma.2023.116355</a>. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study.</p> <p>Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk</p>
A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019
<p>Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p<0.05) and MOD10C2 during 2001–2019 (r=0.97, p<0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies.</p> <p>The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if filled by IMS snow climatology.</p> <p>Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day</p>
The challenge of being slow: Effects of tempo, laterality, and experience on dance movement consistency
<p>Data set for the study published in Journal of Motor Behavior.</p>
Rare and declining bee species are key to consistent pollination of wildflowers and crops across large spatial scales
<p>Biodiversity promotes ecosystem function in experiments, but it remains uncertain how biodiversity loss affects function in larger-scale natural ecosystems, where rare and declining species which are likely to be lost and function needs to be maintained across space and time. Here we explore the importance of rare and declining bee species to the pollination of three wildflowers and three crops using large-scale (72 sites across 5,000 km2), multi-year datasets. Half (82/164) bee species were rare or declining, but these species provided ~15% of overall pollination. To determine the number of species important to ecosystem function, we used two methods of 'scaling up', both of which have previously been used for biodiversity-function analysis. First, we summed bee species' contributions to pollination across space and time and then found the minimum set of species needed to provide a threshold level of function across all sites; according to this method, effectively no rare and declining bee species were important to pollination. Second, we account for the "insurance value" of biodiversity by finding the minimum set of bee species needed to simultaneously provide a threshold level of function at each site in each year. The second method leads to the conclusion that 25 rare and eight declining bee species (36% and 53% of all rare and declining bee species, respectively) are important. Our findings provide some of the strongest evidence yet for the importance of rare and declining species, thereby providing a more direct link between real-world biodiversity loss and ecosystem function.</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
Text-fig. 3. Scanning electron micrographs of Normapolles flowers/fruits from Zliv-Řídká Blana locality. a: Budvaricarpus serialis, aggregation of laterally fused fruits supported by a common bract (br – bract, hp – hypanthium), no. NM-F 3160; b: Budvaricarpus sp., aggregation of laterally fused fruits supported by several bracts, no. NM-F 3619; c: Caryanthus trebecnsis, reproductive unit consists of three bisexual, epigynous flowers/young fruits, no. NM-F 3286; d: Dahlergeniantus sp., hypogynous flower with radial symmetry, no. NM-F 4495; e: Calathiocarpus sp., epigynous flower with radial symmetry, no. NM-F 4631; f: Caryanthus sp. 1, ribbed fruit–nut of deltoid shape, no. NM-F 3208; g: Zlivifructus vachae, bisymmetrical flower with four tepals and four stamens, no. NM-F 3172; h: Taxon 3, ribbed fruit of Normapolles affinity, no. NM-F 3724. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 3. Scanning electron micrographs of Normapolles flowers/fruits from Zliv-Řídká Blana locality. a: Budvaricarpus serialis, aggregation of laterally fused fruits supported by a common bract (br – bract, hp – hypanthium), no. NM-F 3160; b: Budvaricarpus sp., aggregation of laterally fused fruits supported by several bracts, no. NM-F 3619; c: Caryanthus trebecnsis, reproductive unit consists of three bisexual, epigynous flowers/young fruits, no. NM-F 3286; d: Dahlergeniantus sp., hypogynous flower with radial symmetry, no. NM-F 4495; e: Calathiocarpus sp., epigynous flower with radial symmetry, no. NM-F 4631; f: Caryanthus sp. 1, ribbed fruit–nut of deltoid shape, no. NM-F 3208; g: Zlivifructus vachae, bisymmetrical flower with four tepals and four stamens, no. NM-F 3172; h: Taxon 3, ribbed fruit of Normapolles affinity, no. NM-F 3724.
No evidence for the consistent effect of supplementary feeding on home range size in terrestrial mammals
<p>Food availability and distribution are key drivers of animal space use. Supplemental food provided by humans can be more abundant and predictable than natural resources. It is thus believed that supplementary feeding modifies the spatial behaviour of wildlife. Yet, such effects have not been tested quantitatively across species. Here, we analysed changes in home range size due to supplementary feeding in 23 species of terrestrial mammals using a meta-analysis of 28 studies. Additionally, we investigated the moderating effect of factors related to i) species biology (sex, body mass, taxonomic group), ii) feeding regimen (duration, amount, purpose), and iii) methods of data collection and analysis (source of data, estimator, spatial confinement). We found no consistent effect of supplementary feeding on changes in home range size. While an overall tendency of reduced home range was observed, moderators varied in the direction and strength of the trends. Our results suggest that multiple drivers and complex mechanisms of home range behaviour can make it insensitive to manipulation with supplementary feeding. The small number of available studies stands in contrast with the ubiquity and magnitude of supplementary feeding worldwide, highlighting a knowledge gap in our understanding of the effects of supplementary feeding on ranging behaviour.</p>
Extended Files for "Lessons from Hubble & Spitzer: 1D Self-Consistent Model Grids for 19 Hot Jupiter Emission Spectra"
<p>This directory includes extended files for Wiser et al. 2024, "Lessons from Hubble & Spitzer: 1D Self-Consistent Model Grids for 19 Hot Jupiter Emission Spectra." </p> <p>Files:</p> <ul> <li><strong>Extended Planet Figures:</strong> For each of the 20 planets discussed in the manuscript, <em>[PlanetName].pdf </em>includes figures showing the secondary eclipse spectra and parameter estimations for each model scenario. A file for Kepler-13Ab illustrates our grid models' inability to explain the WFC3 and Spitzer observations simultaneously. </li> <li><strong>Internal Temperature Tests:</strong><em> InternalTemperatureTests.pdf</em> includes figures illustrating our inability to constrain an atmosphere's internal temperature with these model grids and the WFC3 and Spitzer observations.</li> <li><strong>Parameter Estimates .csv Tables:</strong> <ul> <li>Both .csv files include planet and star parameters (temperatures, radii, mass, logg, semimajor axis) and parameter ranges for each planet grid. They also include parameter estimations for each model scenario. Listed are the medians of each parameter's posterior probability distribution and pos/neg values encompassing the one-sigma confidence region. This information is shown visually in the extended planet figures. </li> <li><em>fiducial_stats.csv</em> includes parameter estimations for the fiducial model scenario. For planets with multiple solutions (as described in Wiser et al. 2024), multiple rows detail each solution. There is also a "limit" flag for parameters with an upper limit, lower limit, or unconstrained (UL, LL, or UC, respectively).</li> <li><em>nonfiducial_stats.csv </em>includes parameter estimates for all other model scenarios. This table does not account for multiple solutions or "limit" flags. </li> </ul> </li> <li>For complete model grids, please contact the authors. </li> </ul>
The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning
<p>Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.</p>
Self Consistent Recurrent Neural Network for Path Dependent Deformation
<p>Data and Machine Learning codes for the paper:</p> <ul> <li>Title<strong> : Self Consistent Recurrent Neural Network for Path Dependent Deformation</strong></li> </ul> <p><strong>Abstract</strong> : Current neural network (NN) structures can learn patterns from data points with historical dependence. Specifically, in natural language processing (NLP), sequential learning has transitioned from recurrence-based architectures to transformer-based architectures. However, it is not known in advance which NN architectures will perform best on datasets containing deformation history due to mechanical loading. Thus, this study ascertains the appropriateness of 1D-convolutional, recurrent, and transformer-based architectures for predicting material failure based on the earlier states in the form of deformation history. Following this investigation, the crucial issues arising from the mathematical computation process of the best-performing NN architectures and the physical properties of the deformation paths are examined in detail. Additionally, we propose a novel and adaptable RNN approach to address the fundamental challenges of truncation and consistency related to obtaining estimations that are compatible with the natural physical properties of deformation paths. This study will serve as a foundation for localization estimation and pave the way for future endeavors to propose further solutions to encountered challenges.</p>
Ten-a-day: bumblebee pollen loads reveal high consistency in foraging breadth among species, sites, and seasons
<p>Pollen and nectar are crucial resources for bees, but vary greatly amongst plant species in their quantity, nutritional quality, and timing of availability. This makes it challenging to identify an appropriate range of plants to meet the nutritional needs of pollinators through the year, though this information is important in the design of pollinator conservation schemes.</p> <p>Using DNA metabarcoding of pollen loads, we record the floral resource use of UK farmland bumblebees at different stages of their colony lifecycle, and compare this with null models of 'expected' resource use based on landscape-scale resource availability (pollen and nectar), to identify foraging priorities and preferences. We use this approach to ask three main questions: i) what is the foraging breadth of individual bumblebees?; ii) do bumblebees utilise a greater or lesser diversity of plant species than expected if they foraged in proportion to resource availability?; iii) which plant species do bumblebees preferentially utilise?</p> <p>Individual bumblebees foraged from a highly consistent number of different plant taxa (mean: 10 ±0.37 SE per bee), regardless of their species, sampling site, or time of year. This high consistency in foraging breadth, despite large changes in the quantity, identity, and diversity of resource availability, implies a strong behavioural tendency towards a fixed range of foraging resources. This effect was most striking in April when foraging diversity was maintained despite very low landscape-level resource diversity.</p> <p>Bumblebees used some plant taxa significantly more than predicted from their landscape-level floral abundance, nectar, or pollen supply, implying certain desirable characteristics beyond the mere quantity of resource. These included <em>Allium</em> spp. and <em>Vicia</em> spp. in April; <em>Trifolium repens</em> and <em>Lotus corniculatus</em> in July; and <em>Cynareae</em> spp. (thistles) and <em>Taraxacum officinale</em> in September.</p> <p>Our results strongly indicate that resource quantity is not the only factor driving bumblebee foraging patterns, and that resource diversity and quality are also important factors. Thus, in addition to providing large quantities of floral resources, we recommend that pollinator conservation schemes also focus on providing a sufficient diversity of preferred floral resources, enabling pollinators to self-select a diverse and nutritious diet.</p>
Figure 12. Repaired shell displaying components consistent with Argonauta nouryi and A in Recognising variability in the shells of argonauts (Cephalopoda: Argonautidae): the key to resolving the taxonomy of the family
Figure 12. Repaired shell displaying components consistent with Argonauta nouryi and A. cornutus: a–d, four perspectives of a single shell (52.3 mm shell length, SBMNH 357476) displaying an initial component consistent with A. nouryi Lorois, 1854 ("nouryi") followed by a subsequent component consistent with A. cornutus Conrad, 1854 ("cornutus"); a, right lateral view; b, oblique right lateral view; c, anterior aperture view; d, oblique ventral keel view. Dashed line represents repair line separating two visually different components. Scale bar = 1 cm.
A Tool for Collaborative Consistency Checking During Modeling (dataset)
<div>This dataset contains a few snapshots as well as the jar files for the tools (server and client).</div> <div> </div> <div>It also contains a image displayng the metamodel representation of the streamlined language (Metamodel-sUML) which is used to define the models created by our sUML modeler.</div> <div> </div> <div> </div> <div> </div> <div><strong>Running the tools:</strong></div> <div> </div> <div><strong><a href="https://isse.jku.at/designspace/index.php/Abstract_Rule_Language">Requirements</a></strong>: </div> <div>Windows 10/11</div> <div>JDK 21 or above</div> <div> </div> <div><strong>How to run the server and add rules:</strong></div> <ul> <li>Run the DesignSpace-Server</li> <li>On the top menu, click Consistency > Rule Editor </li> <li>To add a new rule, select a Language (sUMLv3) and an instance type (e.g., Class)</li> <li>Add a name for the rule</li> <li>Add a definition for the rule (use the ARL language (<a href="https://isse.jku.at/designspace/index.php/Abstract_Rule_Language">https://isse.jku.at/designspace/index.php/Abstract_Rule_Language</a>) and the properties of the UML metamodel</li> <li>Always click validate (if there are erros in the rule defintioj, check the log)</li> <li>If no erros are found, the rule is created.</li> </ul> <div><strong>Run the modeler tools:</strong></div> <ul> <li>When running an instance of the sUML-Modeler, you need to select a user (currently there are four users, more can be added using the DesignSpace server)</li> <li>To connect multiple tools, select different users (selecting the same user will close the previously connected tool as each user can only connect once)</li> <li>Always create a root model before adding other diagrams</li> <li>For changes to be sent to the server, select DesignSpace > Commit from the tool menu</li> <li>The option DesignSpace > Update will pull changes (in case they exist)</li> <li>To enable the highlighting of inconsistencies, select Validate > Display Inconsistencies</li> </ul> <div> </div> <div> </div> <div><strong>Sample rules:</strong></div> <div> </div> <div>Instance Type: class</div> <div>Name: A class must have unique operations</div> <div> </div> <div>self.operations->forAll(o1, o2 | o1 <> o2 implies o1.name <> o2.name)</div> <div> </div> <div>Instance Type: class</div> <div>Name: A class must have unique attributes</div> <div> </div> <div>self.attributes->forAll(a1, a2 | a1 <> a2 implies a1.name <> a2.name)</div> <p> </p>
Shingle example self-consistent source dataset for global domain generation
<p>Self-consistent source dataset for the Shingle project -- an approach and software library for the generation of boundary representation from arbitrary geophysical fields and initialisation for anisotropic, unstructured meshing (see https://www.shingleproject.org for more information).</p>
Fig. 2 in Consistency in fruit preferences across the geographical range of the frugivorous bats Artibeus, Carollia and Sturnira (Chiroptera)
Fig. 2. Distribution of three bat genera (Artibeus, Carollia and Sturnira – solid gray) and the four most frequent plant genera (Cecropia, Ficus, Piper and Solanum – dotted pattern) in their diet in the Neotropical region. Sources: bat distribution follows GARDNER (2008); plant distribution follows JARAMILLO & MANOS (2001) for Piper; KNAPP et al. (2004) for Solanum, LOBOVA et al. (2003) for Cecropia; SHANAHAN et al. (2001) for Ficus.
Fig. 1 in Consistency in fruit preferences across the geographical range of the frugivorous bats Artibeus, Carollia and Sturnira (Chiroptera)
Fig. 1. Number of records for the four fruit genera most frequently consumed by Artibeus, Carollia and Sturnira (total number of records for each bat species) based on literature review.
Data from: Species-soil relationships across Amazonia: niche specificity and consistency in understory ferns
<p>One of the two data files contains occurrence and abundance information for Adiantum and Lindsaea fern species in 1215 sampling units distributed across Amazonia. The other data file contains metadata and soil base cation concentration values. Explanations of the data columns are in the README.txt file.</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.