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2,103 results for “Components”
Data behind The ALCHEMI atlas: principal component analysis reveals starburst evolution in NGC 253
<p>This depository is for additional files of the PCA paper using the ALCHEMI survey.</p> <p>std_datalist.csv: This is a csv file that includes standardized intensities for all the transitions/continua.</p> <p>pca_alchemi_corrmatrix.py: This is a python file to plot a correlation matrix of standardized intensities. It displays a transition pair when you hover the cursor on the matrix element. It uses std_datalist.csv.</p>
Quantitative results of the analysis of relevant components of artificial bilayered substitutes developed by tissue engineering
<p>This dataset corresponds to the quantification results carried out for artificial bilayered substitutes developed by tissue engineering and control tissues analyzed in the manuscript entitled "<span>Spatiotemporal characterization of extracellular matrix maturation in human artificial stromal-epithelial tissue substitutes</span>". Tissue engineering techniques offer new strategies to understand complex processes in a controlled and reproducible system. In this study, we generated bilayered human tissue substitutes consisting of a cellular connective tissue with a suprajacent epithelium (full-thickness stromal-epithelial substitutes or SESS), and human tissue substitutes with an epithelial layer generated on top of an acellular biomaterial (epithelial substitutes or ESS). Both types of artificial tissues were studied at sequential time periods to analyze the maturation process of the extracellular matrix (ECM) using histochemical and immunohistochemical techniques. Results showed that both models were able to exhibit a partial development of the epithelial layer. ESS cells showed active proliferation, positive expression of KRT5 and low expression of differentiation markers, whereas SESS epithelium showed higher differentiation levels, with a progressive positive expression of KRT10 and claudin, although the differentiation levels of control native tissues were not reached. Despite the typical rete-ridges and papillae of native tissues were not found, stromal cells in SESS tended to accumulate and actively synthetize ECM components such as collagens and proteoglycans in the stromal area in direct contact with the epithelium (Z1 zone), whereas these components were very scarce in ESS. Regarding the basement membrane (BM), ESS showed a partially-differentiated structure containing fibronectin-1 (FN1) and perlecan (HSPG2), although the PAS staining signal was significantly lower than control native tissues. However, SESS showed higher BM differentiation, with positive expression of FN1, HSPG2, nidogen 1 (NID1), chondroitin-6-sulfate proteoglycans (CH6S), agrin (AGRN), and collagens types IV (COL-IV) and VII (COL-VII), although this structure was negative for lumican (LUM). These results confirm the relevance of epithelial-stromal interaction for ECM development and differentiation, especially regarding BM components, and suggest the usefulness of bilayered artificial tissue substitutes to reproduce ex vivo the ECM maturation and development process of human tissues. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p> <p> </p>
Different components of cognitive-behavioural therapy affect specific cognitive mechanisms: supporting data
<p>This repository holds data associated with the manuscript "Different components of cognitive-behavioural therapy affect specific cognitive mechanisms" (<a href="https://osf.io/preprints/psyarxiv/ydct5">https://osf.io/preprints/psyarxiv/ydct5</a>).</p><ul><li>rew-eff-planningX .csv files represent data from experiments testing effects of a planning/goal-setting intervention on reward-effort decision-making (X=1, initial discovery sample, X=2, replication sample)</li><li>causal-attr-X .csv files represent data from experiments testing the effects of a cognitive restructuring intervention on causal attribution tendencies (X=1, initial discovery sample, X=2, replication sample)</li><li>crossover-study-X .csv files represent data from the crossover-design study (data saved separately for participants randomly allocated to complete the reward-effort vs causal attribution tasks).</li></ul><p>Data-generating task code and full analysis code can be found at <a href="https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy">https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy</a>.</p>
Structure of the Canadian Forest Fire Weather Index System: the model and its components
<p>This material is part of:</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br><strong>Structure of the Canadian Forest Fire Weather Index system: the model and its components</strong> — The <a href="../record/10806780#preview-iframe">figure below</a> (formats: <a href="../record/10806780/files/FWI-sys_simple_diagram.png?download=1">PNG</a> or <a href="../record/10806780/files/FWI-sys_simple_diagram.pdf?download=1">PDF</a>) shows the logical subdivision of the Canadian Forest Fire Weather Index system (FWI-sys) in components.</p> <p> </p> <p>The Canadian FWI-sys (De Groot,1987; Van Wagner,1987) is an index of fire danger by weather designed to consider the effects on vegetation fuels of the sequence of weather conditions. It is designed to estimate a uniform numerical rating for the relative fire potential accounting for the local sequence of temperature, wind speed, relative humidity, and precipitation, for the day in which the rating is estimated but also modelling the dynamics of the previous days. In addition, the variable amount of possible drying due to the varying solar irradiation in different seasons is taken into account by adjusting the parameters per each month of the year.<br><br>The system is standardised to consider the behaviour of a reference typology of vegetation fuel (mature pine stand) regardless of other non-weather factors which may locally influence the fire danger, such as the specific topography or the pattern, composition, and structure of vegetation assemblages. Therefore, FWI-sys is suitable to support the harmonised comparison among variable weather conditions, either spatially (comparing different spatial regions) or temporally (comparing the same region over time).<br><br>The FWI-sys components are organised in three layers, processing at the daily frequency weather information (either from observations, reanalysis, forecast, or climate scenarios) and estimating from it a final standard aggregated numerical rating of fire intensity.<br><br>The required input variables are</p> <ul> <li>Temperature T (nominally, FWI-sys requires T at noon)</li> <li>Wind speed W (nominally, FWI-sys requires T at noon)</li> <li>Relative humidity</li> <li>Precipitation (24-hour rainfall)</li> <li>Month of the year</li> </ul> <p>The FWI-sys was originally designed to fit the Candian conditions. Following its success, adaptations of the system were studied for different areas of the globe. This implies that the parameters used inside the FWI-sys globally also depend on the latitude (Alexander, 2008).</p> <p>The first layer of components (the <em>fuel moisture codes</em>: Fine Fuel Moisture Content, FFMC; Duff Moisture Code, DMC; Drought Code, DC) is composed by dynamic variables. This means that the value of each component for a given day depends also on the value of the same component the day before. The dynamic components with longer memory of their past history also approximate the seasonal changes in solar radiation, by considering the month of the year (see Figure, bottom left).</p> <ul> <li><strong>Fine Fuel Moisture Code (FFMC)</strong> : provides a numerical rating of the moisture content of the top litter and other cured fine fuels, indicating the relative ease of ignition and flammability of fine fuel.</li> <li><strong>Duff Moisture Code (DMC)</strong> : models a standard moisture content of loosely-compacted organic layers of moderate depth (duff layers and medium-sized woody material). This component of the FWI-sys represents wooden fuels of intermediate thickness.</li> <li><strong>Drought Code (DC)</strong> : models a standard moisture content of deeper, compact, organic layers. This component of the FWI-sys is able to track seasonal drought effects on coarse wooden fuels.</li> </ul> <p> </p> <p>The second layer of components (the<em> fire behaviour indices</em>: Initial Spread Index, ISI; Buildup Index, BUI; Fire Weather Index, FWI) mathematically is composed by stateless D-TM components. This means that these components do not have an internal memory of the past conditions, while instead they rely on the combined information offered by the different temporal inertia of the fuel moisture codes, which they process as input information.</p> <ul> <li><strong>Initial Spread Index (ISI)</strong> : represents the expected rate of fire spread. It considers the combined effects of wind and the FFMC on the rate of spread. However, it excludes the influence of fuel moisture and availabity for the coarser wooden fuels.</li> <li><strong>Buildup Index (BUI)</strong> : combines DMC and DC to model the total amount of fuel available for combustion to the spreading fire.</li> <li><strong>Fire Weather Index (FWI)</strong> : offers a standard aggregated numerical rating of fire intensity which combines ISI and BUI.</li> </ul> <p><br>Given its structure, the model can also be interpreted as a recurrent neural network (RNN) where the input variables are transformed into the final aggregated numerical rating (FWI) by means of two hidden layers: the <em>fuel moisture codes</em> (three nodes/neurons); and the <em>fire behaviour indices</em> (two nodes/neurons).</p> <p>Note that this structure is not a simple feedforward network, as the first hidden layer is made by dynamic components (FFMC, DMC, DC, see highlighted feedack loops in <a href="../record/10806780/files/FWI-sys_simple_diagram_recurrent.png?download=1">PNG</a> format). The activation functions are complex, and the D-TM components (either dynamic or stateless) generally mix physically-based and empirical aspects. A consequence of the complexity of the FWI-sys activation functions is that a neural network with standard (e.g. sigmoidal) activation functions would need to exploit disproportionally many more additional neurons for the same FWI-sys D-TM complexity to be reasonably approximated.</p> <p> </p> <p>An additional FWI-sys component is a simple transfromation of the aggregated FWI values to better account for the nonlinear increase of fire control effort with increasing FWI values (Van Wagner, 1987):</p> <ul> <li><strong>Daily Severity Rating (DSR)</strong>: this transformation of FWI is meant to provide a measure of control difficulty:<br> DSR = 0.0272 ⋅ FWI <sup>1.77</sup><br>which easily invertible:<br> FWI = ( DSR / 0.0272 ) <sup>1 / 1.77</sup></li> </ul> <p><br><br>To cite the Figure, please refer to:<br><br>de Rigo, 2016. <strong>Structure of the Canadian Forest Fire Weather Index System: the model and its components</strong>. https://doi.org/10.5281/zenodo.6558576</p> <p>which is part of</p> <p>de Rigo, D. 2018. <strong>The Canadian Forest Fire Weather Index System: a synopsis of computational semantics</strong>. https://doi.org/10.6084/m9.figshare.4046673<br><br> </p> <p> </p> <p><strong>References</strong></p> <p>De Groot, W.J., 1987. <strong>Interpreting the Canadian Forest Fire Weather Index (FWI) System</strong>. In: <em>Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar, Proceedings</em>. Winnipeg, Manitoba, Canada, pp. 3-14. <a href="https://purl.org/INRMM-MiD/c-14176512">https://purl.org/INRMM-MiD/c-14176512</a> </p> <p>Van Wagner, C.E., 1987. <strong>Development and structure of the Canadian Forest Fire Weather Index System</strong>. <em>Forestry Technical Report</em>. Canadian Forestry Service, Ottawa, Canada. <a href="https://purl.org/INRMM-MiD/c-14168337">https://purl.org/INRMM-MiD/c-14168337</a> </p> <p>Alexander, M.E., 2008. <strong>Latitude considerations in adapting the Canadian Forest Fire Weather Index System for use in other countries</strong>. In: Lawson, B.D., Armitage, O.B. (Eds.), <em>Weather Guide for the Canadian Forest Fire Danger Rating System</em>. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, Canada, pp. 67–73. ISBN:978-1-100-11565-8 <a href="https://purl.org/INRMM-MiD/z-MBDA6A6I">https://purl.org/INRMM-MiD/z-MBDA6A6I</a></p> <p> </p>
A dataset of three vine water status indicators, weather records and soil water capacity components collected from a rain-fed Mediterranean vineyard
<p>This dataset contains three key indicators of vine water status: vine shoot growth index (iG-Apex), predawn leaf water potential (Ψpd), and carbon isotope ratio (δ13C). Additionally, it includes weather data and soil measurements. Spatial data files of studied fields, plots, and some vines’ locations are also provided. The data were collected from a rain-fed vineyard in Southern France, 4 km north of the Mediterranean Sea. Measurements were made at the plot level, with each plot consisting of 10 adjacent grapevines (Vitis Vinifera). The iG-Apex was recorded weekly from June 10 to August 22, 2022, across 70 vine plots. Ψpd was measured weekly between 3 a.m. and 5 a.m. in 12 of these plots using a pressure chamber. On August 23, 2022, 100 berries were sampled from each plot, and 1.5 mL of grape juice was extracted for δ13C analysis using a carbon analyzer and mass spectrometer. The dataset contains 761 iG-Apex measurements (70 time series), 720 Ψpd measurements (60 time series), and 70 single-date δ13C measurements. Weather data were recorded daily in 2022 from a weather station located at the vineyard's center, providing five parameters: cumulative rainfall, relative humidity, and three air temperatures (mean, minimum, and maximum). Soil available water capacity components (horizon thickness, field capacity, permanent wilting point, bulk density, and rock fragment content) were measured in 5 of the 70 plots after prior soil profile wall analyses. Monitoring vine water status is essential for optimizing grape yield and wine quality. While Ψpd and δ13C are considered reference methods, they are expensive and prone to logistical constrains. In contrast, iG-Apex can be collected with minimal time and financial investment. This dataset enables not only the exploration of statistical relationships between the plant-based and soil-based indicators, but also the modeling of Ψpd and/or δ13C using iG-Apex, while accounting for weather and soil influences. All data were georeferenced, allowing future integration of ancillary spatial data sources, like multi-spectral remote sensing images or yield data.</p>
Hands-On! Video demonstration - Paper folding components
<p>This video demonstrates all possible actions for the <strong>Paper folding task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong>Hands-On!</strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong>Hands-On!</strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a></p>
Hands-On! Video demonstration - Writing components
<p><span>This video demonstrates all possible actions for the <strong>Writing task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Hands-On! Video demonstration - Paper cutting components
<p><span>This video demonstrates all possible actions for the <strong>Paper cutting task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Distinguishing GUI Component States for Blind Users using Large Language Models
<p><strong># Data Code Repository</strong></p><p> </p><p>This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.</p><p> </p><p><strong>## Features</strong></p><p> </p><p>- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes</p><p>- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.</p><p>- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.</p><p> </p><p><strong>## Installation</strong></p><p> </p><p>To use the data code, you can down or clone the required code.</p><p>Notably, before using the code, make sure the necessary environment configuration is done.</p><p> </p><p><strong>## Dependencies</strong></p><p>The data code has the following dependencies:</p><p> </p><p>Python (version 3.6 or higher)</p><p>NumPy</p><p>Pandas</p><p>Seaborn</p><p>Scikit-learn</p><p>Openai</p><p>Android Studio (version 4.0)</p><p> </p><p>Install the required dependencies using pip:</p><p>pip install numpy..</p><p> </p><p><strong>##License</strong></p><p>This data code is distributed under the MIT License. See LICENSE for more information.</p><p> </p><p><strong>##Copyright</strong></p><p>All copyright of the tool is owned by the author of the paper.</p>
Input data for PARASO, a circum-Antarctic fully-coupled 5-component model
<p>Input data for running the PARASO experiments.</p> <p>These files should be extracted, and the folder containing them should be referred to in the `data.cfg` Coral configuration file. See also PARASO documentation from the PARASO sources.</p> <p>The ERA5 forcings (COSMO boundary files and NEMO surface forcings) are not provided herein as they are too large, but we provide:</p> <p>- scripts for downloading and post-processing the ERA5 NEMO forcings;</p> <p>- INT2LM configuration file, with the new Antarctic geometry, to generate COSMO lateral forcings.</p> <p>A 3-month sample of ERA5 data is also available (see <strong>Forcings</strong> below).</p> <p><strong>Model description: </strong>Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.</p> <p><strong>Source code (no COSMO)</strong>: Pelletier, Charles, Klein, François, Zipf, Lars, Haubner, Konstanze, Mathiot, Pierre, Pattyn, Frank, Moravveji, Ehsan, & Vanden Broucke, Sam. (2021). PARASO source code (no COSMO) (v1.4.3). Zenodo. <a href="https://doi.org/10.5281/zenodo.5576201">10.5281/zenodo.5576201</a></p> <p><strong>Forcings: </strong>Pelletier, Charles, & Helsen, Samuel. (2021). PARASO ERA5 forcings (1.4.3) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5590053">10.5281/zenodo.5590053</a><br> </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>ORAS5: </strong>Zuo, H, Alonso-Balmaseda, M, Mogensen, K, Tietsche, S: OCEAN5: The ECMWF Ocean Reanalysis System and its Real-Time analysis component. 2018. <a href="https://doi.org/10.21957/la2v0442">10.21957/la2v0442</a> downloaded from the <a href="https://www.cen.uni-hamburg.de/en/icdc/data/ocean/easy-init-ocean/ecmwf-oras5.html">ICDC</a> (University of Hamburg) on 01-SEP-2019. <em>(The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.)</em></p> <p><strong>BedMachine: </strong>Morlighem, M. 2020. <em>MEaSUREs BedMachine Antarctica, Version 2</em>. Ice-shelf Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/E1QL9HFQ7A8M">10.5067/E1QL9HFQ7A8M</a>. Accessed 01-DEC-2019.</p> <p>Morlighem, M., E. Rignot, T. Binder, D. D. Blankenship, R. Drews, G. Eagles, O. Eisen, F. Ferraccioli, R. Forsberg, P. Fretwell, V. Goel, J. S. Greenbaum, H. Gudmundsson, J. Guo, V. Helm, C. Hofstede, I. Howat, A. Humbert, W. Jokat, N. B. Karlsson, W. Lee, K. Matsuoka, R. Millan, J. Mouginot, J. Paden, F. Pattyn, J. L. Roberts, S. Rosier, A. Ruppel, H. Seroussi, E. C. Smith, D. Steinhage, B. Sun, M. R. van den Broeke, T. van Ommen, M. van Wessem, and D. A. Young. 2020. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, <em>Nature Geoscience</em>. 13. 132-137. <a href="https://doi.org/10.1038/s41561-019-0510-8">10.1038/s41561-019-0510-8</a></p> <p><strong>Iceberg forcings: </strong>Jourdain, Nicolas C., Merino, Nacho, Le Sommer, Julien, Durand, Gaël, & Mathiot, Pierre. (2019). Interannual iceberg meltwater fluxes over the Southern Ocean (1.0) [Data set]. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.3514728">10.5281/zenodo.3514728</a></p> <p>Merino N., Jourdain, N. C., Le Sommer, J., Goose, H., Mathiot, P. and Durand, G (2018). Impact of increasing Antarctic glacial freshwater release on regional sea-ice cover in the Southern Ocean. <em>Ocean Modelling</em>, 121, 76-89. <a href="https://doi.org/10.1016/j.ocemod.2017.11.009">10.1016/j.ocemod.2017.11.009</a></p>
Salinity and horizontal components of velocity of the eastern part of the Black Sea
<p>The high resolution Black Sea circulation in 2008 - 2009 from numerical simulations obtained with NEMO modeling framework v 3.6 [Madec et al., 2016]. Such a resolution was used in order to reproduce meso- and submesoscale dynamics in the so-called Euxinus cascade, including basins of the Azov, Black and Marmara Seas [Mizyuk, Puzina, 2019; Mizyuk, Korotaev, 2020].</p> <p>Below the brief description of the developed regional configuration is presented. The horizontal grid is a quasi-uniform geographical mesh with a resolution of 1/96° × 1/69° northward and eastward correspondingly. The model bottom topography is based on bathymetric data from the EMODnet v1 digital elevation model (URL: <a href="http://www.emodnet-bathymetry.eu/">http://www.emodnet-bathymetry.eu</a>). To reproduce adequate flow values through the Bosphorus Strait, the "partially closed cell” technique was used [Madec et al., 2016]. The calculation was carried out for the period 2008-2009. For vertical descritisation we used a partial step z-coordinate. The time step of 1 min was chosen.</p> <p>Vertical turbulent mixing was performed using the k – ε model [Rodi, 1987]. The lasteral diffusion on momentum and tracers described with the bilaplacian operator. The corresponding values for turbulent viscosity and diffusivity are (-4 10<sup>7</sup> m<sup>4</sup>/s) and (-8 10<sup>6</sup> m<sup>4</sup>/s).</p> <p>The vector form was used for momentum equations with an energy and enstrophy conserving scheme. Advection terms of tracer equations are calculated with the TVD scheme [Zalesak, 1979] . The UNESCO formula is used as the equation of state. The sea surface height is calculated using split-explicit scheme.</p> <p>The initial temperature and salinity for the Black Sea (BS) basin were taken from BS Marine Forecasting Center (<a href="http://mis.bsmfc.net:8080/thredds/catalog.html">http://mis.bsmfc.net:8080/thredds/catalog.html</a>). For the Sea of Marmara the initial conditions are taken from the product of the Global Ocean reanalysis of CMEMS (<a href="http://www.marine.coperniucs.eu/">www.marine.coperniucs.eu</a>).The initial conditions for the basin of the Azov Sea were considered as climate data obtained using an optimal interpolation procedure of all available in-situ measurements provided by CMEMS in-situ TAC and SeaDataNet project (<a href="https://www.seadatanet.org/">https://www.seadatanet.org/</a>).</p> <p>Surface boundary conditions were obtained the product of ECMWF ERA5 reanalysis [Hersbach et al., 2020] with the spatial resolution of 1/4° and time resolution of 1 h. Open boundary conditions for Marmara Sea.</p>
Query dan Perspektif Bloom untuk "Analisis Data Paradise Papers Indonesia Menggunakan Algoritma Strongly Connected Components dan Harmonic Centrality"
<p>Query dan Perspektif Bloom untuk "Analisis Data Paradise Papers Indonesia Menggunakan Algoritma Strongly Connected Components dan Harmonic Centrality"</p>
InnoRate_Multi-component web-based survey_Dataset4_2021.12.28_v1
<p>This dataset has been collected by deploying an online survey to gather insight and analyse the needs and preferences of potential users and stakeholders of the InnoRate Platform of the InnoRate Project (H2020 GA 821518). We have gathered responses from 3 main stakeholder groups, namely innovators, investors and innovation intermediaries. The surveys for each stakeholder group (.docx format), along with the respective anonymised version of their responses (.xlsx format) are uploaded on Zenodo.</p>
The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components
<p>The excel file <em>Attributes_manuscript.csv</em> contains the mean annual variables and the catchments' attributes used in the manuscript: "<strong>The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components</strong>". Details of each attributes are indicated in the .txt file <em>Attributes_description.txt</em>.</p>
IPCC AR6 Relative Sea Level Projections without Background Component
<p><strong>Description</strong></p> <p>This data set contains detailed elements the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WG1 9.6.3.2, as well as p-boxes derived from these distributions.</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Predator-based selection and the impact of edge sympatry on components of coral snake mimicry
<p>Mimicry is a vivid example of how predator-driven selection can impact phenotypic diversity, which itself can be influenced by the presence (sympatry) or absence (allopatry) of a dangerous model. However, the impact of sympatry and allopatry on predation on mimicry systems at fine spatial scales (e.g., edge sympatry, allopatry) is not well understood. We used a clay model study in a montane tropical site in Honduras to test the impact of edge sympatry on 1) overall attack rates, 2) the fitness benefit of mimetic coloration, 3) predation on specific mimetic signal components, and 4) temporal variation in predator-based selection on mimicry components. Unlike previous research, we found that mimetic phenotypes received significantly more attacks than cryptic replicas in edge sympatry, suggesting that mimetic phenotypes might not confer a fitness benefit in areas of edge sympatry. Additionally, we documented temporal variation in predator-based selection, as the impacts of allopatry on predatory attacks varied among years. Our results imply that the effect of sympatry and allopatry on predator-based selection in mimicry systems may be more complex than previously thought for species-rich assemblies of coral snakes and their mimics in the montane tropics.</p>
Comparative analysis of Printed Circuit Boards with Surface and Embedded Components under Natural and Forced Convection
<p>Figures of heat distribution on PCB depending on the installation method (surface and embedded) and the speed of forced airflow.</p>
TERMINUS WP5: Chain Extension Kinetics for Solvent-Free Adhesive Components
<p>In two-component polyurethane (PUR) adhesives, polyols and isocyanates are reacted to produce binding media.In order to obtain a Solvent-Free prepolymer as one of PUR components, an excess of macrodiols can be reacted with diisocyanates.The addition reaction between OH and NCO functional groups is frequently called “Chain-Extension”.PUR polymerization rarely follows classical kinetic theories, therefore, experimental study was carried out to monitor the rate of polymerization.Small specimens of the reaction mixture were periodically withdrawn during the course of Chain Extension.The amount of isocyanates was measured by titration.Four different macrodiols and two diisocyanates were tested at several temperatures and concentrations.Some data on two reactions from previous Dataset 5-1 “Adhesive_Chain_Extension_WP5” is also included to broaden the range of studied temperatures. Collected data can be used for future kinetic studies of both Solvent-Based and Solvent-Free PUR adhesives.</p>
Limited stability of multi-component brines on the surface of Mars
<p>This data package contains all the data used for the following publication: </p> <p>Limited stability of multi-component brines on the surface of Mars</p> <p>Vincent F. Chevrier<br> Alec Fitting<br> Edgard G. Rivera-Valentín</p> <p>The data are evaporation simulations of multicomponent mixtures of composition as measured by the Phoenix Wet Chemistry Laboratory instrument at various temperatures and for three different models (described in the manuscript). </p> <p>The organization and detailed description of the data is presented in the text file AAS35439R1_ReadMe.TXT</p>
Liquid film rupture beyond the thin-film equation: a multi-component lattice Boltzmann study - Dataset
<p>Dataset for the material presented in the article "Liquid film rupture beyond the thin-film equation: a multi-component lattice Boltzmann study"</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.