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5,526 results for “information”
MengxiaoZhao_Using sky-wave echoes information to extend HFSWR's maximum detection range
<p>1. for Figure 2,3,4</p> <p>a. 'Ground Attention.mat' —— The data of ground wave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> Attenuation: 501*4 —— the ground wave attenuation for four different frequency<br> ]</p> <p>b. 'Skywave Attention.mat' —— The data of skywave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> attenuation: 34*4 —— the skywave attenuation for four different frequency<br> ]</p> <p>c. 'Path attenuation of 5Mhz.mat' —— four paths' attenuation of 5Mhz<br> [<br> L —— Ground distance<br> path1 —— the attenuation of path 1<br> path23 —— the attenuation of path 2&3<br> path4 —— the attenuation of path4<br> ]</p> <p><br> 2. for Figure 7 - simulation result</p> <p>a. 'simulation_echoes data.mat' —— the data of simulation targets' echoes</p> <p>b. 'Figure7_data.mat' —— the simulation results<br> [<br> data —— Doppler*Range<br> Doppler —— the axis of Doppler <br> Range —— the axisof Range <br> ]</p> <p>3. for Figure 8,9,10 - actual data processing results. We give 5 batches of echoes' data and the final result of Figure 9.</p> <p>a. 'TCDat1.mat','TCDat2.mat','TCDat3.mat','TCDat4.mat','TCDat5.mat',<br> —— the echoes' data</p> <p>b. 'Figure9_data.mat' —— the final result of Figure 9. <br> [<br> data ——Doppler*Range<br> axist_Doppler —— the axisof Doppler<br> axist_Range —— the axisof Range<br> counT —— the number of detections<br> mTgt —— the parameters of detections<br> ]</p> <p>4. 'parameters.txt' —— the parameters of simulation data and actual data</p> <p><br> </p>
All-sky information content analysis for novel passive microwave instruments - data
<p>This dataset is the underlying data for the article:</p> <p>Grützun, V., S. A. Buehler, L. Kluft, M. Brath, J. Mendrok, and P. Eriksson (in press, 2018), All-sky Information Content Analysis for Novel Passive Microwave Instruments in the Range from 23.8 GHz up to 874.4 GHz, Atmos. Meas. Tech., doi:10.5194/amt-2017-377. </p> <p>Please refer to that article for a description of the scientific background of the data and to the attached README file for a technical documentation. </p> <p>Contact: Verena Grützun, verena.gruetzun@uni-hamburg.de<br> </p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
ISO TR 21965 Information and documentation -- Records management in enterprise architecture - Archi tool project file
<p>This file is a project file, in XML format, of the freeware too Archi (version 4.2.0) modeling the ArchiMate diagrams present in the ISO/DTR 21965:2019.</p> <p>Archi tool is freely available from https://www.archimatetool.com</p> <p>The purpose of the ISO/TR 21965:2019 is to provide a common reference for Records managers (or information managers in general) and Enterprise architects about requirements for records processes and systems. The goal is to establish the Records manager as a key stakeholder in Enterprise Architecture, by expressing the related Records Management Viewpoint.</p> <p>This viewpoint makes use of the concepts of “concerns” and “system of concerns” as defined in ISO/IEC/IEEE 42010:2011, and of the concepts of “stakeholders”, “viewpoint, “view” and “model” as also defined coherently in that standard and in the main Enterprise Architecture references of TOGAF and ArchiMate. With reference to ArchiMate, the main scope of this viewpoint is the Motivational aspect and the layers Strategy and Business, with minor considerations for the layers of Application and Implementation. The Open Group Architecture Framework (TOGAF) is used to inform how this Records Management Viewpoint relates to the Architecture Development Method (ADM).</p> <p>The edition of the file is work of the author, but the intelectual content of the file is the resulting of the work of the ISO working group responsible by the production of the Technical Report: ISO/TC 46/SC 11/WG 14 - Records requirements in Enterprise Architecture</p> <p> </p>
Large scale and information effects on cooperation in public good games
<pre>This dataset accompanies the paper "Large scale and information effects on cooperation in public good games", https://doi.org/10.1038/s41598-019-50964-w It records participant decisions in a set of Public Goods Games (see details in the paper). Explanation of the data fields: #participant_id: identification number for each participant in each treatment #player_alive: (for each round) 1=participant is still playing; 0=participant has been banned for forgetting three decisions or did not show up the first day. #group_avg_contribution: Average contribution #player_contribution: participant individual contribution to the PGG #round_number: round number #gender #age #treatment: see the publication for details regarding the different treatments # IBSEN metadata # 2017A2ECOCOOPANSA01ONL0000ESPMAD </pre>
TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks
<p>The TwitCID collection consists of five Twitter datasets which were extracted from the 1 percent of tweets from Twitter API. </p> <p>The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords “iphone”, “gucci” and “galaxys” respectively. </p> <p>We publish these datasets on behalf of our academic institution – IRIT, France and for the sole purpose of non-commercial research under the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). In accordance with Twitter's Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached.</p> <p>If you would like to use this collection, please cite our paper: </p> <p>Hoang, T. B. N., Mothe, J., & Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 88-100). Springer, Cham.</p>
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations - supplementary information
<p>This repository contains the supplementary data to the following publication:</p> <p>"A Topological Data Analysis Perspective on Non-Covalent Interactions in Relativistic Calculations", by the same authors.</p>
Project files provided as supporting information to the manuscript "A deep learning approach to the structural analysis of proteins"
<p><strong>README file to the project files provided as supporting information to the manuscript “A deep learning approach to the structural analysis of proteins”</strong></p> <p>Dec. 30, 2018</p> <p>Authors: Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p> </p> <p>- datasets.zip: archive containing five .csv files, namely:</p> <p> - decoys_cm.csv : all the data for 10728 protein decoys, training set</p> <p> - evaluation_cm.csv : all data for 146 proteins in the evaluation set</p> <p> - random_CG.csv : 1200 Coulomb matrices. 100 CG models for each protein with 120 amino acids</p> <p> - 1e5g_centered_sphere.csv : 100 CG models in which the central atoms in 1e5g are not removed</p> <p> - 1e5g_random_sphere.csv : 10 CG models for 10 different (random) locations for the sphere that includes atoms that have to be retained. 100 CG models in total</p> <p> </p> <p>- decoys_labels.lab containing the labels associated to the 10728 decoys present in the training set</p> <p>- evaluation_labels.lab containing the labels associated to the 146 pdb files in the evaluation set</p> <p>- random_CG_labels.lab containing the labels associated to the 6 proteins with 120 amino acids</p> <p>- network_development_training: a python script that performs cross validation and full training of the model</p> <p>- saved_networks.zip FOLDER containing 10 networks: the architecture is included in .json files while weight parameters are inside .hs files</p> <p> </p> <p>- pdb_files.zip FOLDER containing the PDB files that have been employed in the project, namely:</p> <p> - pdb_files_len100 : pdb files with 100 amino acids</p> <p> - pdb_files_len101-110 : pdb files with a number of amino acids between 101 and 110</p> <p> - decoys : decoys of length 100 extracted from the above folder: name syntax == PDBNAME_decoy_STARTRES_ENDRES.pdb</p> <p> EXAMPLE 6gsp.pdb will give rise to 6gsp_decoy_0_100.pdb , 6gsp_decoy_1_101.pdb , 6gsp_decoy_2_102.pdb , 6gsp_decoy_3_103.pdb , 6gsp_decoy_4_104.pdb</p> <p> - pdb_files_len100 : 6 pdb files with 120 amino acids</p> <p> </p>
The Structure of Sub-nm Platinum Clusters at Elevated Temperatures (Supplementary Information)
<p><strong><em>This dataset consists of raw data and denoised scanning transmission electron microscopy videos of sub-nm sized clusters of Pt on a carbon substrate. The data is used in the article "The Structure of Sub-nm Platinum Clusters at Elevated Temperatures" published in Angewandte Chemie International Edition, 2019, DOI:10.1002/anie.201911068</em></strong><strong><em> </em></strong></p> <p><strong>Video S1.</strong> A typical sub-nm amorphous cluster at room temperature. 0.5 nm scale bar.</p> <p><strong>Video S2.</strong> Two typical crystalline sub-nm clusters at 350°C. 0.5 nm scale bar.</p> <p><strong>Video S3. </strong>In this high-speed recording at 147 fps, the high beam current required for this fast imaging has suppressed the crystallinity of the cluster, despite the temperature of 350°C. 0.5 nm scale bar.</p> <p><strong>Video S4. </strong>The unusually stable 13-atom cluster at the bottom forms an fcc cuboid, and can be seen rotating at three orientations, as shown by the inset model and in Fig. 3a-c. 0.5 nm scale bar.</p> <p><strong>Video S5. </strong>This 15-atom cluster initially forms an fcc cube, then transforms into multiple hcp structures. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p> <p><strong>Video S6. </strong>The cluster in this movie is a 22-atom truncated rectangular cuboid. 0.5 nm scale bar.</p> <p><strong>Video S7. </strong>In the center and bottom, two 6-atom octagons are rotating (shown in Fig. S4) as they add onto their larger neighboring clusters. The 13-atom cluster in the top forms an unusually stable fcc cuboctahedron from frame 219. 0.5 nm scale bar.</p> <p><strong>Video S8. </strong>The cluster on the bottom left forms a fleeting icosahedron-like structure. 0.5 nm scale bar.</p> <p><strong>Video S9. </strong>This cluster shows fcc structures, despite being recorded at 200°C, but with a very low beam dose. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
Catalog Data for Prior-Informed AGN-Host Spectral Decomposition Using PyQSOFit
<p>This catalog contains 76,565 AGN-host decomposed spectral measurements for all quasars with z<0.8 in SDSS DR16Q. Our prior-informed decomposition method significantly improved the decomposition success rate from less than 60% to 94%. For the first time, we perform the AGN-host spectral decomposition on survey scale catalog.</p> <p>Our spectral decomposition results are highly consistent to those of HSC image decomposition. Our catalog suggests that an average host galaxy contribution at 5100A is 38.8%, which would lead to an overestimation of 0.215 dex in L5100 and 0.219 dex in black hole mass if the host is not removed. The Dn4000 and stellar velocity dispersion measurements from the decomposed host galaxy spectra are also provided.</p> <p>Please read this paper for more techinique details: <a href="https://arxiv.org/abs/2406.17598">arXiv: 2406.17598</a></p>
Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000
<p>Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils.</p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p><a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a></p> <p><strong>Custom code used for digital soil mapping and validation is available on GitHub:</strong></p> <p><a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a></p> <p><strong>Description of the files:</strong></p> <p>The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps.</p> <table> <tbody> <tr> <td> <p><strong>SOC property maps</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Year</strong></p> </td> <td> <p><strong>Filename</strong></p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCc_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCd_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>1992</p> </td> <td> <p>SOCs_0_30cm_1992_q95.tif</p> </td> </tr> <tr> <td> <p>SOC content map</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC content, lower limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC content, upper limit of the 90% PI</p> </td> <td> <p>[g ∙ kg<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCc_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC density map</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC density, lower limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC density, upper limit of the 90% PI</p> </td> <td> <p>[kg ∙ m<sup>-3</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCd_0_30cm_2000_q95.tif</p> </td> </tr> <tr> <td> <p>SOC stock map</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_pred.tif</p> </td> </tr> <tr> <td> <p>SOC stock, lower limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q05.tif</p> </td> </tr> <tr> <td> <p>SOC stock, upper limit of the 90% PI</p> </td> <td> <p>[tons ∙ ha<sup>-1</sup>]</p> </td> <td> <p>2000</p> </td> <td> <p>SOCs_0_30cm_2000_q95.tif</p> </td> </tr> </tbody> </table> <p> </p>
Integrated Taxonomic Information System (ITIS): ITIS version 26 September 2023
ITIS provides authoritative taxonomic information on plants, animals, fungi, and microbes of North America and the world. It is a partnership of U.S., Canadian, and Mexican agencies (ITIS-North America); other organizations; and taxonomic specialists. ITIS is also a partner of Species 2000 and the Global Biodiversity Information Facility (GBIF). The ITIS and Species 2000 Catalogue of Life (CoL) partnership is proud to provide the taxonomic backbone to the Encyclopedia of Life (EOL). <p></p>https://www.itis.gov/
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment, Supporting Information
<p>This repository contains the dataset and analysis scripts associated with the upcoming publication titled <em>Application of optical tweezer technology reveals that PfEBA and PfRH ligands, not PfMSP1, play a central role in Plasmodium-falciparum merozoite-erythrocyte attachment</em>. The repository includes a comprehensive collection of data and scripts related to optical tweezer experiments, growth assays, qPCR data, and supplementary information. It is organized into several sections, each detailing different aspects of the study:</p> <ul> <li><strong>Growth Assays:</strong> Includes raw and processed data on parasitemia levels, invasion rates, and growth rate assays, along with corresponding Jupyter notebooks and Python scripts for data visualization (e.g., <code>GrowthAssayPlotlib.py</code>, <code>Plot GA1.ipynb</code>, and <code>GA2_df_melted.json</code>).</li> <li><strong>qPCR Data:</strong> Contains results from multiple qPCR runs, including quantification data for various samples, as well as analysis scripts and plotted results (<code>qpcr_plotbench.ipynb</code>, <code>qPCR_plotting.py</code>, etc.). Data files such as <code>.xlsx</code> and <code>.json</code> contain gene expression data and fold changes to NF54.</li> <li><strong>Optical Tweezer Experiments:</strong> Includes detailed results and plots from optical tweezer measurements of attachment forces, time dependence, and multiple merozoite attachments. Notebooks (<code>tweezer_plots.ipynb</code>, <code>Antibody_binding_assay_plots.ipynb</code>) and data files support these analyses.</li> <li><strong>Optical Tweezer Images</strong>: Includes images that were used to measure RBC diameters for deformation and force measurements in <code>.tiff</code> format.</li> <li><strong>Supplementary Information (SI):</strong> Provides additional data and visualizations, such as scatter plots of two stretched RBCs, time post-egress vs. detachment force, and antibody GIA flow data. The accompanying figures (e.g., <code>SupFig1d_egress time vs force_3D7.svg</code>, <code>SupFig5a_GIA.svg</code>) are provided as <code>.svg</code> files.</li> </ul> <p>This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights.</p>
Supporting information of a study for the definition and evaluation of a graphical user interface for housing co-design
<p>This dataset is from a study that intends to define, prototype and test a graphical user interface for a housing co-design system. To define the requirements of the interface, we conducted interviews with professionals of architecture, urbanism and social sciences areas, as well as with housing cooperatives and inhabitants of these institutions. An interface solution was prototyped, tested and refined. Then we conducted a heuristic evaluation and a summative evaluation. Such evaluations involved the testing of a high-fidelity prototype, to receive feedback from UX/UI experts, potential users (inhabitants) and architects.</p> <p>S1_File refers to the interview protocol used with the three groups of interviewees. We share the English and Portuguese versions of the interviews with professionals and the original (Portuguese) and translated versions of the remaining ones since these were conducted in Portuguese.</p> <p>S2_File is a dataset reporting the results of the interviews. Each question includes the answers given and the identification (anonymized) of the interviewees who responded to that question.</p> <p>S3_File describes the usability issues identified by the experts during the heuristic evaluation of the high-fidelity prototype. The first page organizes the issues by severity (left) and priority (right). The remaining pages have a table for each issue, including rows for problem designation, heuristic violated, problem description, solution proposal, severity degree, and an image of the interface pointing to the referred issue.</p> <p>S4_File refers to the results of the heuristic evaluation. It includes the identification of each issue, which expert (anonymized) identified such issue, and the heuristic it violates, with the sum of the times each heuristic was violated at the end of each column. At the right, a table presents the consolidation of issues, organized by priority, with columns identifying the issue, severity level, frequency, and priority.</p> <p>S5_File is the script given to potential users to experiment with the interface during the summative evaluation. This script guides the user through the tasks to perform since the prototype does not have all the features functioning.</p> <p>S6_File refers to the questionnaires applied during the summative evaluation with inhabitants. It includes a preliminary questionnaire, a Single Ease Question (SEQ) questionnaire, a System Usability Scale (SUS) questionnaire, and a Graphical User Interface (GUI) questionnaire.</p> <p>S7_File refers to the results of the summative evaluation with inhabitants (potential users).</p> <ul> <li>Page A refers to the preliminary questionnaire with demographic information such as age, gender, education, relationship with digital technologies, etc. Each field corresponds with each inhabitant (anonymised) and the sum and percentage. In the middle, a table presents a summary of the consolidation. In the right possible relations are presented. </li> <li>Page B presents the results of the SEQ questionnaire, identifying the ratings each inhabitant (anonymized) gave each task. A summary of such values is at the right. </li> <li>On page C, the result of each rating for the SUS questionnaire given by each inhabitant (anonymized) is shown. At the bottom is the calculation of the SUS score.</li> <li>Page D presents the GUI questionnaire results for each inhabitant (anonymized), with the average and SD identified for each question. A summary of such results is on the right.</li> <li>Page E holds the notes taken by the researchers based on their observations regarding task performance. The information is organized in tables for each step of each task and includes the completeness, attempts, and time taken for each inhabitant (anonymized) to complete such task. Also, the sum, percentage, average, and SD are registered. Next to each task is a table identifying how many participants accomplished the task at the first attempt.</li> <li>Page F refers to the strong and weak aspects identified by the inhabitants. Strong and weak aspects are identified, as well as which inhabitant (anonymized) has identified them. The sum and percentage are also given. At the right, there is a table with the consolidation of results by combining similar answers. </li> </ul> <p>S8_File refers to the results of the discussion with architects after experiencing the interface. Such results relate to the positive and negative aspects that the architects identified in the interface and its usefulness for architecture. The left table identifies the strong and weak aspects that architects (anonymized) identified and the sum and percentage associated with them. The table on the right consolidates such results, with similar responses combined.</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
JGR_biogeosciences_Supplementary_Information_Field_Data
<p>The dataset is the supplementary material as an Excel file, which includes the field measurements of DOC concentration, CDOM absorption coefficient, remote-sensing reflectance, along with the corresponding coordinates, dates, and times of collection. The study area is the Plum Island Estuary in Massachusetts and the Gulf of Maine (USA).</p>
Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context - Supporting Information S2 and S3
<p>The data contains the databases used to calculate the climate change impacts of second-life batteries including full Life Cycle Inventory data published in the article entitled "Powering the Circular Future: Climate Change and Economic Perspectives on Second-Life Batteries in the Belgian Context".</p> <p>The second file S3 contains the economic data and the climate change impacts of the same article.</p> <p>In version 2.0 of S2, a sensitivity analysis and more detail is added in the results.</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.