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4,479 results for “hybrids”
MXene and MoS3−x Coated 3D-Printed Hybrid Electrode for Solid-State Asymmetric Supercapacitor
<p>All raw dataset of the published article "MXene and MoS3−x Coated 3D-Printed Hybrid Electrode for Solid-State Asymmetric Supercapacitor", DOI: 10.1002/smtd.202100451</p>
Raw data for the plots in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process"
<p>Raw data for the plots in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process"</p> <p>https://doi.org/10.1021/acsami.1c24392 </p> <p>ACS Appl. Mater. Interfaces 2022, 14, 19295−19303</p>
Data from "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad"
<p>This upload includes data shown in the figures of the Paper "Tracking the Vector Acceleration with a Hybrid Quantum Accelerometer Triad".</p>
Data for "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment"
<p>This upload includes the data presented and analyzed in the article "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment" by Jakub Fojt, Tuomas P. Rossi, Mikael Kuisma, and Paul Erhart.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.7118376">doi:10.5281/zenodo.7118376</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Open Access levels of Dutch universities' output 2016-2017 (articles & reviews): green, gold, hybrid and bronze - May 2018
<p>Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.</p> <p>Our previous analysis (<a href="http://doi.org/10.5281/zenodo.1133759">10.5281/zenodo.1133759</a> and <a href="http://doi.org/10.7287/peerj.preprints.3520v1">10.7287/peerj.preprints.3520v1</a>) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels. </p> <p>In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories (IRs) (see <a href="https://unpaywall.org/sources">https://unpaywall.org/sources</a>). This now includes coverage of the IRs from all Dutch universities. </p> <p>Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis. </p> <p>In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands. </p> <p>The following categories are distinguished (description taken from Piwowar at al., 2018, doi: <a href="https://doi.org/10.7717/peerj.4375">10.7717/peerj.4375</a>)</p> <ul> <li><strong>Pure gold</strong>: Published in an open-access journal (as defined by the DOAJ)</li> <li><strong>Hybrid</strong>: Free under an open license in a toll-access journal</li> <li><strong>Bronze</strong>: Free to read on the publisher page, but without a license</li> <li><strong>Green: </strong>Available from an institutional or disciplinary repository (including PubMedCentral)</li> </ul> <p>Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data <a href="https://unpaywall.org/products/simple-query-tool">Simple Query Tool</a>. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared. </p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Supporting data and codes for: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression
<p>This GitHub repository includes R codes and datasets for the paper: A new biological species in the Mercurialis annua polyploid complex: functional divergence in inflorescence morphology, hybrid sterility and possible introgression</p>
Data of the publication Rare Earth‐Diamond Hybrid Structures for Optical Quantum Technologies
<p>Data of the publication published under the reference: I.G. Balașa et al., Advanced Optical Materials, 2401487 (2024).</p>
Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends
<p>This dataset was used in the publication <em>"Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends"</em>. An overview of the abbreviations and the dataset can be found below.</p>
Structural build-up and stability of hybrid monoglyceride-triglyceride oleogels
<p>This dataset was used in the publication <em>"</em><em>Structural build-up and stability of hybrid monoglyceride-triglyceride oleogels</em><em>"</em>. An overview of files is given below:</p>
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Hybrid LCA database generated using ecoinvent and EXIOBASE
<p>Hybrid LCA database generated using ecoinvent and EXIOBASE, i.e., each process of the original ecoinvent database is added new direct inputs (coming from EXIOBASE) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.</p> <p>For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).</p> <p><em>Why are there two versions for hybrid-ecoinvent3.5?</em></p> <p>One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE.</p> <p><em>What does capital endogenization do?</em></p> <p>It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).</p> <p><em>So which version do I use?</em></p> <p>Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.</p> <p><em>Why is it only available for hybrid-ecoinvent3.5?</em></p> <p>The work used for capital endogenization is not available for exiobase3.8.1.</p> <p><em>How do I use the dataset?</em></p> <p>First, to use it, you will need both the corresponding ecoinvent [cut-off] and EXIOBASE [product x product] versions. For the reference year of EXIOBASE to-be-used, take 2011 if using the hybrid-ecoinvent3.5 and 2019 for hybrid-ecoinvent3.6 and 3.7.1.</p> <p>In the four datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.</p> <p>Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)</p> <p>Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)</p> <p>If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the <em>normalized</em> capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).</p> <p>Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:</p> <p><span class="math-tex">\(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \\ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \\ 0 \end{bmatrix} \right) \end{equation}\)</span></p> <p>q<sup>hyb</sup> gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.</p> <p>C<sup>lca</sup> and C<sup>io</sup> are the respective characterization matrices for ecoinvent and EXIOBASE.</p> <p>S<sup>lca</sup> and S<sup>io</sup> are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.</p> <p>I is the identity matrix.</p> <p>A<sup>lca</sup> and A<sup>io</sup> are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).</p> <p>K<sup>io</sup> is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.</p> <p>C<sup>u</sup> (or upstream cut-offs) is the matrix that you get in this dataset.</p> <p>C<sup>d</sup> (or downstream cut-offs) is simply a matrix of zeros in the case of this application.</p> <p>Finally you define your final demand (or functional unit/set of functional units for LCA) as y<sup>lca</sup>.</p> <p><em>Can I use it with different versions/reference years of EXIOBASE?</em></p> <p>Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent (which were used to compile the hybrid databases, for all versions presented here) are defined in €2005.</p> <p><em>What are the weird suite of numbers in the columns?</em></p> <p>Ecoinvent processes are identified through unique identifiers (uuids) to which metadata (i.e., name, location, price, etc.) can be retraced with the appropriate metadata files in each dataset package.</p> <p><em>Why is the equation (I-A)<sup>-1</sup> and not A<sup>-1</sup> like in LCA?</em></p> <p>IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.</p> <p><em>Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?</em></p> <p>You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339</p> <p>If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367</p> <p><em>It's too complicated...</em></p> <p>The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.).<strong> As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.</strong></p> <p><em>Where can I get more information?</em></p> <p>The whole methodology is detailed in (Agez et al., 2021).</p>
Global hybrid forest mask for the year 2000
<p>A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there was no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at <a href="http://biomass.geo-wiki.org/">http://biomass.geo-wiki.org</a>.</p> <p>More details can be found in the paper:</p> <p>Schepaschenko D., See L., Lesiv M., McCallum I., Fritz S., et al. (2015). Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. <em>Remote Sensing of Environment </em>162 208-220. <a href="https://doi.org/10.1016/j.rse.2015.02.011">https://doi.org/10.1016/j.rse.2015.02.011</a>.</p> <p>The data set consists of following files:</p> <p>1. for2000_bg.zip - Global forest mask "best guess" - percentage forest cover at a 1 km spatial resolution for the year 2000;<br> 2. for2000_ca_cou.zip - Global forest mask calibrated to the FAO FRA statistics at national scale;<br> 3. for2000_ca_reg.zip - Global forest mask calibrated to the FAO FRA statistics at continental scale;<br> 4. training_pc.csv - training data, which contains visual interpretation of very high resolution imagery at 20159 locations;<br> 5. validation.csv - validation data, which contains visual interpretation of very high resolution imagery at 1816 locations.</p>
Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)
<p>Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre.</p> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>Forecast are described in detail by the following publications:</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological Research Letters, 78(1):37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters, 78(1): 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological Research Letters, 78(1): 78-86.</p> <p>Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological Research Letters, 78(1): 94-98.</p> <p>Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological Research Letters, 78(1):116-120.</p> <p>Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133.</p> <p>Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.</p>
Data of hybrid vesicles fusion for Nano Letters' journal article
<p>Dataset to accompany the manuscript "Thermoplasmonic induced vesicle fusion for investigating membrane protein phase affinity"</p>
A hybrid 100-m global land cover dataset with Local Climate Zones for WRF
<p>This hybrid 100-m CGLC-MODIS-LCZ global land cover dataset is produced for the Weather Research and Forecasting (WRF) model starting from version 4.5. It is based on 1) the Copernicus Global Land Service Land Cover (CGLC, Buchhorn et al., 2021) product resampled to MODIS IGBP classes (CGLC-MODIS), and 2) the global map of Local Climate Zones (LCZ, Demuzere et al., 2022a, b) that describes the urban and built-up land surface. Both the CGLC and LCZ products are available at a 100-m spatial resolution, are representative for the year 2018, and cover -180°W to 180°E and -60°S to 78°N. Remaining areas are filled with the MODIS land cover classes. This dataset has been implemented into the WRF Preprocessing System (WPS) as <a href="https://www2.mmm.ucar.edu/wrf/users/download/get_sources_wps_geog.html">tiled binary data files</a> with <a href="https://github.com/wrf-model/WPS/blob/develop/geogrid/GEOGRID.TBL.ARW_LCZ">a new GEOGRID table entry</a> to allow WRF/WPS users to flexibly use this dataset in their studies particularly for urban modeling applications.</p> <p>To display the dataset in QGIS, <em>cmap_Qgis_CGLC_MOD_LCZ.txt </em>can be used as a color scheme.</p> <p>For more details, please read the technical documentation: <a href="https://doi.org/10.5281/zenodo.7670792">https://doi.org/10.5281/zenodo.7670792</a>.<br> <br> References:</p> <p><em>Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.-E., Li, L., Tarko, A. Copernicus Global Land Service: Land Cover 100m: version 3 Globe 2015-2019: Product User Manual (Dataset v3.0, doc issue 3.4). Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963<br> <br> Demuzere M, Kittner J, Martilli A, et al. A global map of local climate zones to support earth system modelling and urban-scale environmental science. Earth Syst Sci Data. 2022a;14(8):3835-3873. doi:10.5194/essd-14-3835-2022</em></p> <p><em>Demuzere M, Kittner J, Martilli A, et al. (2022). Global map of Local Climate Zones (2.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364593</em></p>
Time and momentum resolved characterization of hybrid plasmonic heterostructure Au/WSe2
<p>Dataset attached to paper titled "Observation of Multi-Directional Energy Transfer in a Hybrid Plasmonic-Excitonic Nanostructure" with time and momentum characterization of a 2D palsmonic heterostructure formed by Au nanoislands on bulk WSe2. It contains Angle-resolved photoemission spectroscopy (ARPES) and time-resolved ARPES data (trARPES.zip); femtosecond electron diffraction (FED) data (FED.zip); optical absorption spectroscopy data (Optical_absorbance.zip) and Transmission electron microscopy micrographs (TEM.zip).</p> <p>For <strong>trARPES.zip</strong>, the following table reports the grid of measurements and most important parameters:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Sample temperature (K)</strong></td> <td> <p><strong>Pump Wavelength (nm)</strong></p> </td> <td><strong>Pump Duration (fs)</strong></td> <td><strong>Material</strong></td> </tr> <tr> <td>trARPES_Metis_002.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2124.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2146.mpes.nxs</td> <td>70</td> <td>800</td> <td>35</td> <td>WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2197.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2198.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2212.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan2219.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3159.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3164.mpes.nxs**</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe<sub>2</sub></td> </tr> <tr> <td>trARPES_Phoibos_Scan3185.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>WSe<sub>2</sub></td> </tr> </tbody> </table> <p>*These scans are acquired with higher angular dispersion requiring separate scans for K and Sigma valleys.</p> <p>**Fluence scan.</p> <p><strong>FED.zip</strong> contains the following subfolders:</p> <ul> <li><em>Manuscript_Figure</em>: Experimental data and fit parameters depicted in Figure 4 of the main article.</li> <li><em>Analysis</em>: Additional information for the FED data including: raw data descriptions (delay, power, filename and more), Matlab scripts with comments, masks and backgrounds for image processing. The <em>Static_patterns</em> subfolder contains electron diffraction patterns of pure WSe<sub>2</sub> flakes and Au-covered WSe<sub>2</sub> flakes.</li> </ul> <p><strong>Optical_absorbance.zip</strong> contains the following subfolders & subfiles:</p> <ul> <li><em>without Au</em> & <em>with Au</em> containing all the optical measurements of pristine and Au-covered WSe<sub>2</sub> flakes, respectively.</li> <li><em>comparison_with_and_without_Au.xlsx</em> contains the analysis of the difference curves</li> <li><em>manuscript_figure.txt </em>contains the data that were used in Figure 1 of the main article.</li> </ul> <p> </p>
Dataset for 'Zinc hybrid sintering for printed transient sensors and wireless electronics'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Zinc hybrid sintering for printed transient sensors and wireless electronics”.</p> <p>This work aims to study and develop a method for the efficient sintering of printed zinc metal, with the aim to facilitate the fabrication of biodegradable electronics by additive manufacturing. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste, and present opportunities for the fabrication of novel bioresorbable medical devices that can harmlessly degrade in the body and eliminate the need for re-operation. The method that is presented in this publication combines electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. Several sensors are shown as demonstrators (temperature, strain, pressure). The data that was collected in the frame of this work is present in this repository. It relates to both the study of the process introduced above as well as the characterization of the demonstrators. More information about the contents of the dataset is present in the included README files.</p>
Dataset of "The Impact of Spacer Size on Charge Transfer Excitons in Dion-Jacobson and Ruddlesden-Popper Layered Hybrid Perovskites"
<p>This dataset underpins the following article published in the Journal of Physical Chemistry Letters:</p> <p>"The Impact of Spacer Size on Charge Transfer Excitons in Dion-Jacobson and Ruddlesden-Popper Layered Hybrid Perovskites"</p> <p>DOI: 10.1021/acs.jpclett.3c01125</p> <p> </p> <p>The dataset contains steady state absorption (UV/Vis), transient absorption (TA) and electroabsorption (EA) data acquired from experiments on 2D perovskites incorporating different organic spacers. The dataset also includes data acquired from temperature dependent measurements.</p> <p>The transient absorption data has been treated using a home-written matlab script in order to correct for the chirp.</p> <p> </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.