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3,172 results for “Publications”
Preliminary results: Predicted cooling effect, lives saved and associated economic value from public green spaces in Paris
<p><span>This dataset represents preliminary results predicting the cooling effect from public green spaces in Paris for the 10th, 22nd and 24th July 2019, the lives saved and associated economic valuation.</span></p> <p>The data format is a shapefile with coordinate reference system RGF93 v1 / Lambert-93 (EPSG:2154).</p> <p>Please see the README file for description of the variable names. </p> <p>Full results will be shared in due course, alongside all code and a detailed description of the method. If you would like to know more about the method or would like to be informed when the full results and method are published, please contact Jo Garrett at j.k.garrett@Exeter.ac.uk</p>
Data associated with the publication "A de novo designed coiled-coil-based switch regulates the microtubule motor kinesin-1"
<p> All raw data required to reproduce the findings in the manuscript ""A de novo designed coiled-coil-based switch regulates the microtubule motor kinesin-1".</p>
Dataset for interface calculations as BSON mongodump and JSON formats for the publication: "High-throughput generation of potential energy surfaces for solid interfaces"
<p>This dataset that contains the results presented in the journal article entitled "High-throughput generation of potential energy surfaces for solid interfaces" that was published in Volume 207 of the Elsevier journal Computational Materials Science.</p> <p>The dataset consists of a dump of a MongoDB database with a single collection that contains data on 6 solid interfaces including the generalized stacking fault energies, corrugation, interface distances and adhesion sites for the film and the substrate at minimum and maximum adhesion energy configurations, and images of the full potential energy surface (PES).</p> <p>The data is served in two different formats; a BSON mongodump folder that can be restored to a MongoDB instance using the mongorestore tool, and additionally as simple .json files. The contents are identical and the users are encouraged to choose the format that is convenient for them.</p>
Scientific Webinar on Sustainable Public Food Procurement (SPFP) in the European Union
<p>On 23 April 2024, <a href="https://sapiensnetwork.eu/">SAPIENS Network</a> <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sustainability-to-collective-table/">Early Stage Researcher Chiara Falvo</a> held a scientific webinar focusing on Sustainable Public Food Procurement (SPFP) in the European Union. The event took place in hybrid form and was hosted within the Master’s Course in Food Systems Law at the Department of Law of the University of Turin, also in collaboration with the Department of Agricultural, Forestry and Food Sciences (DISAFA). After a brief introduction on public procurement law and practice, Chiara delved into the legal strategies and mechanisms for integrating social and environmental considerations into the procurement of food and catering services. She also highlighted some national and local experiences that are leading the way in the field. During the event, <a href="https://www.giurisprudenza.unito.it/do/docenti.pl/Alias?silvia.mirate#tab-profilo">Professor Silvia Mirate</a>, who also acted as a discussant, provided an overview on the new EU Deforestation Regulation (EUDR), followed by Chiara's exploration of its relevance for public procurement. Contributing to bridging the gap between the scientific domains of law and agricultural and forestry sciences, this webinar may be relevant for students and newcomers to public procurement, especially in the food sector, as well as for anyone interested in understanding deforestation issues and the latest legal mechanisms to combat them. </p>
Smart Cable Air (SCA) and Smart Cable Water (SCW) public data sets, april 2024
<p>These datasets are supporting material for the publication<br>"An ASIC-based system-in-package MEMS gas sensor with impedance spectroscopy readout and AI-enabled identification capabilities"<br>by CNR-IMM Bologna, Univerisity of Pisa and Sensichips.</p> <p>These are some data sets as acquired to date using the Smart Cable Air and Smart Cable Water devices, for public use by data scientists and AI developers.</p> <p>SCA.zip contains datasets for the "Smart Cable Air" sensor: <a href="https://sensichips.com/air-sensor/">https://sensichips.com/air-sensor/</a></p> <p>SCW.zip contains datasets for the "Smart Cable Water" sensor: <a href="https://sensichips.com/water-sensor/">https://sensichips.com/water-sensor/</a></p> <p>The contents are described in the readme files contained in the subdirectories.</p>
Dataset accompanying the publication "Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy"
<p>This dataset accompanies the following publication:</p> <p>Hülagü, D., Tobias, C., Dao, R., Komarov, P., Rurack, K., Hodoroaba, V.-D., Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy. Sci.Rep, <span>14<span>, 17936 (2024), https://doi.org/10.1038/s41598-024-68797-7.</span></span></p> <p>It contains SEM and AFM-in-SEM images of polystyrene (PS) core particles, polystyrene-iron oxide (PS/Fe3O4) core-shell particles, and polystyrene-iron oxide-silica (PS/Fe3O4/SiO2) core-shell-shell particles. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.Com/denizhulagu/roughness-analysis-by-electron-microscopy.</p> <p> </p> <p>The investigated particles were produced at BAM laboratories as previously described in:</p> <p>Hülagü, D. et al. Generalized analysis approach of the profile roughness by electron microscopy with the example of hierarchically grown polystyrene–iron oxide–silica core–shell–shell particles. Adv. Eng. Mater. 24, 2101344, https://doi.org/10.1002/adem.202101344 (2022).</p> <p>Tobias, C., Climent, E., Gawlitza, K. & Rurack, K. Polystyrene microparticles with convergently grown mesoporous silica shells as a promising tool for multiplexed bioanalytical assays. ACS Appl. Mater. Interfaces 13, 207, https://dx.doi.org/10.1021/acsami.0c17940 (2020).</p>
Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia
<p>This repository contains the Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia.</p> <p>Authors: M.W. Vaughan<sup>a</sup>, H. Lim<sup>a</sup>, B. Pham<sup>a</sup>, R. Seede<sup>a</sup>, A. T. Polonsky<sup> a</sup>, K. Johnson<sup> a</sup>, P. J. Noell<sup>a,*</sup></p> <p>a: Sandia National Laboratories, Albuquerque, NM 87123</p> <p>Each folder contains a ReadMe.txt describing the files and their organization within each folder. </p> <p><br>Acknowledgements:<br><span>This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. </span></p>
Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"
<p>Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"</p>
Canada_construction_emissions_inventory_[Public_Data]
<p><strong>Overview</strong>:</p> <p>This data was created using the code stored at the following <a href="https://github.com/Leopoldwdb/Canada_construction_emissions_inventory">GitHub</a>. The code applies OpenIO-Canada to Canadian Supply-Use Tables to create EEIO matrices. It then performs additional analyses on said matrices to obtain consumption-based accounts of GHG emissions driven by Canadian construction sectors, as well as data on the geographic flows and GDP intensity of embodied emissions in construction.</p> <p><strong>File descriptions:</strong></p> <p><strong>1. T2_Figure2_data.csv </strong>A file containing the data represented in Figure 2. </p> <p><strong>2. T3_Figure3_data.csv </strong>A file containing the data represented in Figure 3. </p> <p><strong>3. T4_Figure4_data.csv </strong>A file containing the data represented in Figure 4. </p> <p><strong>4. T5_Figure5_data.csv </strong>A file containing the data represented in Figure 5. </p> <p><strong>5. T6_Figure6_data.csv </strong>A file containing the data represented in Figure 6. </p> <p><strong>6. T7_Figure7_data.csv </strong>A file containing the data represented in Figure 7. </p> <p><strong>7. CanCons_workbook_V2.xlsx</strong></p> <p>A workbook summarizing all the analyses performed to create the figures in the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector". Contains all the data exported by the jupyter notebook stored at the GitHub link, as well as the additional analysis & formatting steps taken.</p> <p><strong>8. RAW_data-for-analysis</strong><em><strong>_</strong></em><strong>figure2-7.zip</strong></p> <p>A Zip file containing the raw data created by <a href="https://github.com/Leopoldwdb/Canada_construction_emissions_inventory/blob/main/CanCons_Analysis_notebook.ipynb">CanCons_Analysis_notebook.ipynb</a> and used by the CanCons_workbook (above) to produce the figures in question. This includes:</p> <ul> <li><strong>Figure2_RAW_data.csv</strong></li> </ul> <p>A direct export of the D matrix, along with totals.</p> <ul> <li><strong>Figure3_RAW_data.csv</strong></li> </ul> <p>Results of a contribution analysis of the final demand for construction-based Gross Fixed Capital Formation (GFCF) from all Canadian provinces and territories. Results show, for construction demand in each province, all the environmental impacts driven by each construction sub-sector.</p> <ul> <li><strong>Figure3_RAW_data_GDP.csv</strong></li> </ul> <p>Accompanies Figure3_RAW_data.csv. Contains data from the Y matrix on the final demand for construction-based GFCF in $. Used for calculating intensities per unit GDP used in Table 1.</p> <ul> <li><strong>Figure4_RAW_data_[construction sector].csv</strong></li> </ul> <p>3 files which contain the results of contribution analyses of 3 individual construction sectors: Roads & Highways, Communications Infrastructure, and Residential Buildings. Results show, for each province, the embodied environmental impacts associated with the inputs into these 3 sectors.</p> <ul> <li><strong>Figure5+6_RAW_data_Baseline.csv</strong></li> </ul> <p>Results of a standard contribution analysis of Construction GFCF to serve as a baseline for the following files.</p> <ul> <li><strong>Figure5+6_RAW_data_Zero[Region].csv</strong></li> </ul> <p>Each file represents the result of a contribution analysis for Construction GFCF where the S matrix values for the [Region] (representing the environmental impacts caused by supply-chain steps within a region) have been zeroed out. This means that the results in these files represent a world where the [Region]'s contribution to the final impacts of all other regions have been removed. Subtracting these values from the baseline results in values representing each [Region]'s contribution to consumption-based impacts driven by construction every other region. This allows the flows of embodied emissions in construction materials to be mapped.</p> <ul> <li><strong>Figure7_RAW_data.csv</strong></li> </ul> <p>Subset of results from Figure3_RAW_data on the distribution of energy (TJ) and emissions (kgCO2) across regions and construction sectors.</p> <p> </p> <p><strong>9. IO_system.zip</strong></p> <p>A Zip file containing CSVs of all the matrices which represent the EEIO system. These include the A, Y, S, FY matrices, as well as matrices which were used in the development of the IO system (Z, F, U, V, g, q), and the matrices representing the results of the analysis and the characterization of impacts (C, D, E). For more information on these matrices please see the methodology section of the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector".</p> <p>The .zip includes the following matrices:</p> <ul> <li>A.csv - Technology matrix (normalized Z matrix).</li> <li>C.csv - Characterization matrix.</li> <li>D.csv - Matrix of total characterized environmental impacts associated with each category of final demand.</li> <li>E.csv - Matrix of total environmental impacts associated with each category of final demand.</li> <li>F.csv - Matrix of direct impacts associated with the production of each product category.</li> <li>FY.csv - Matrix of direct impacts associated with final consumption.</li> <li>L.csv - Leontief inverse.</li> <li>S.csv - Matrix of direct impacts associated with the production of each product category normalized by dollar of input.</li> <li>U.csv - Use matrix for intermediates (value of products used by industries in 2018 Canadian Dollars).</li> <li>V.csv - Supply matrix for intermediates (products supplied by industries in Can$). </li> <li>Y.csv - Matrix of spending by final demand categories.</li> <li>Z.csv - Input-Output table for Canada.</li> <li>g.csv - Diagonalized vector representing total industry output in Can$.</li> <li>q.csv - Diagonalized vector of total product output (also known as x vector).</li> </ul> <p>Canadian regional abbreviations:</p> <ul> <li>'CA-AB' - Alberta</li> <li>'CA-BC' - British Columbia</li> <li>'CA-MB' - Manitoba</li> <li>'CA-NB' - New Brunswick</li> <li>'CA-NL' - Newfoundland and Labrador</li> <li>'CA-NS' - Nova Scotia</li> <li>'CA-NT' - Northwest Territories</li> <li>'CA-NU' - Nunavut</li> <li>'CA-ON' - Ontario</li> <li>'CA-PE' - Prince Edward Island</li> <li>'CA-QC' - Quebec</li> <li>'CA-SK' - Saskatchewan</li> <li>'CA-YT' - Yukon</li> </ul> <p>Country codes for international trade:</p> <ul> <li>AT Austria<br>BE Belgium<br>BG Bulgaria<br>CY Cyprus<br>CZ Czech Republic<br>DE Germany<br>DK Denmark<br>EE Estonia<br>ES Spain<br>FI Finland<br>FR France<br>GR Greece<br>HR Croatia<br>HU Hungary<br>IE Ireland<br>IT Italy<br>LT Lithuania<br>LU Luxembourg<br>LV Latvia<br>MT Malta<br>NL Netherlands<br>PL Poland<br>PT Portugal<br>RO Romania<br>SE Sweden<br>SI Slovenia<br>SK Slovak Republic<br>GB United Kingdom<br>US United States<br>JP Japan<br>CN China<br>CA Canada<br>KR South Korea<br>BR Brazil<br>IN India<br>MX Mexico<br>RU Russian Federation<br>AU Australia<br>CH Switzerland<br>TR Turkey<br>TW Taiwan<br>NO Norway<br>ID Indonesia<br>ZA South Africa<br>WA RoW Asia and Pacific<br>WL RoW America<br>WE RoW Europe<br>WF RoW Africa<br>WM RoW Middle East</li> </ul>
Simulation results with the EULAG research model for the publication: "Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost"
<p>Supplementary material for the publication</p> <ul> <li>Mark Schlutow, Tobias Stacke, Tom Doerffel, et al. Large eddy simulations of the interaction between the Atmospheric Boundary Layer and degrading Arctic permafrost. ESS Open Archive . January 24, 2024. <a href="https://doi.org/10.22541/essoar.170612558.81370785/v1">https://doi.org/10.22541/essoar.170612558.81370785/v1</a></li> </ul> <p>The material contains all simulation results and raw outputs that are necessary to reproduce the figures and statistics of the publication. </p>
Raw data for publication: Weathering of Wood Modified with Acetic Anhydride – Physical, Chemical, and Aesthetical Evaluation
<p>Color.xlsx</p> <p>This file contains CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Weather data.xlsx</p> <p>This file contains daily local weather conditions in San Michelle, Italy during the natural weathering test.</p> <p>Gloss.xlsx</p> <p>This file contains the gloss value measured on the surface of wood samples.</p> <p>contact angle and surface energy.xlsx</p> <p> This file contains dynamic contact angle and surface energy measured on the surface of wood samples.</p> <p>TGA.xlsx</p> <p>This file contains the Thermogravimetric (TG) and derivative thermogravimetric (DTG) data of the wood samples.</p> <p>Surface roughness.xlsx</p> <p>This file contains the roughness value of wood samples.</p>
Publications data
<p>The file provides the number of scientific publications about mangroves by country and year, for the 13 mangrove rich countries from 1975 to 2023. </p>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
WISC WFS Rhinovirus Publication Data
<p>Preliminarily processed peptide array IgA or IgG antibody reactivity against 67 proteins in tab-separated values format as described in <em><a href="https://doi.org/10.3389/fimmu.2024.1385121" target="_blank" rel="noopener">Assessing immune factors in maternal milk and paired infant plasma antibody binding to human rhinoviruses</a>.</em></p> <p>Sample metadata and analysis code available in the linked GitHub repository.</p>
The metabolomics raw data and a supporting statistical analyses data set for publication: Metabolomic analysis revealed the absence of the principal antimicrobial compound of Pseudomonas donghuensis P482, 7-hydroxytropolone, under restricted nutrient conditions.
<p><a href="../api/records/11220997/draft/files/Metabolomic%20analyses%20raw%20files.zip/content" target="_blank" rel="noopener noreferrer">Metabolomic analyses raw files</a>, Compounds analyses, Hierarchical Condition tress and PCA Scores are uploaded.</p>
Public tags added to resources in Trove, 2008 to 2024
<p>This dataset contains details of 2,495,958 unique public tags added to 10,403,650 resources in <a href="https://trove.nla.gov.au/">Trove</a> between August 2008 and June 2024. I harvested the data using the Trove API and saved it as a CSV file with the following columns:</p> <ul> <li>`tag` – lower-cased text tag</li> <li>`date` – date the tag was added</li> <li>`zone` – API zone containing the tagged resource</li> <li>`record_id` – the identifier of the tagged resource</li> </ul> <p>I've documented the method used to harvest the tags in <a href="https://github.com/GLAM-Workbench/trove-lists/blob/master/harvest-tags.ipynb">this notebook</a>.</p> <p>Using the `zone` and `record_id` you can find more information about a tagged item. To create urls to the resources in Trove:</p> <ul> <li>for resources in the 'book', 'article', 'picture', 'music', 'map', and 'collection' zones add the `record_id` to `https://trove.nla.gov.au/work/`</li> <li>for resources in the 'newspaper' and 'gazette' zones add the `record_id` to `https://trove.nla.gov.au/article/`</li> <li>for resources in the 'list' zone add the `record_id` to `https://trove.nla.gov.au/list/`</li> </ul> <p>Notes:</p> <ul> <li>Works (such as books) in Trove can have tags attached at either work or version level. This dataset aggregates all tags at the work level, removing any duplicates.</li> <li>A single resource in Trove can appear in multiple zones – for example, a book that includes maps and illustrations might appear in the 'book', 'picture', and 'map' zones. This means that some of the tags will essentially be duplicates – harvested from different zones, but relating to the same resource. Depending on your needs, you might want to remove these duplicates.</li> <li>While most of the tags were added by Trove users, more than 500,000 tags were added by Trove itself in November 2009. I think these tags were automatically generated from related Wikipedia pages. Depending on your needs, you might want to exclude these by limiting the date range or zones.</li> <li>User content added to Trove, including tags, is available for reuse under a CC-BY-NC licence.</li> </ul> <p>See <a href="https://github.com/GLAM-Workbench/trove-lists/blob/master/analyse_tags.ipynb">this notebook</a> for some examples of how you can manipulate, analyse, and visualise the tag data.</p>
3D Laser Scanning Data: Public Square in Murcia and Engineering Laboratory at the University of Alicante
<p>This dataset includes 3D terrestrial laser scans obtained using the Leica C10 ScanStation. The data covers two distinct scenarios:</p> <ol> <li> <p><strong>Public Square in Murcia Capital</strong>: This dataset includes two scan positions within a public square located in Murcia. Three HDTarget markers were placed, and their center or vertex coordinates are provided in the accompanying _vertices.txt file. The scans were conducted with the laser scanner leveled, but they are not registered.</p> </li> <li> <p><strong>Engineering Laboratory at the University of Alicante</strong>: This dataset consists of two scans of the Ground Engineering Laboratory at the University of Alicante. The scans were conducted with the same leveled laser scanner, and no targets were used. Between the two scans, some elements in the laboratory were slightly moved, which can be identified by comparing the point clouds.</p> </li> </ol>
Data for publication "A unified surface tension model for multi-component salt, organic and surfactant solutions"
<p>This repository contains the data of the publication:</p> <p>Title: "A unified surface tension model for multi-component<br>salt, organic and surfactant solutions"<br>Authors: Judith Kleinheins, Claudia Marcolli, Cari Dutcher, Nadia Shardt<br>Date: 2024</p>
Simulation results for "Integration of Plant and Microbial Oil Processing at Oilcane Biorefineries for More Sustainable Biofuel Production" publication
<p>Simulation results for "Integration of Plant and Microbial Oil Processing at Oilcane Biorefineries for More Sustainable Biofuel Production" publication.</p>
Dataset accompanying the publication: River mouth hydrodynamics: the role of the outlet geometry and transient tidal and river discharge conditions on the jet structure
<p>Delft3D model input files for all numerical scenarios tested. Subfolders contents:</p> <p>00_example: files to run simulation 111 (see Table 1 in the original manuscript). The remaining cases can be simulated using the correct combination of files contained in the following subfolders.</p> <p>01_grid: grid files for the different geometries defined</p> <p>02_bathymetry: bathymetry files for the different geometries defined</p> <p>03_Boundary conditions: files containing the node information where the boundary conditions are defined for each case.</p> <p>04_bct: files containing the time series for the boundary conditions</p> <p>05_bch: files containing the information on the harmonic conditions defined at the offshore boundary condition.</p> <p>06_Discharge: files containing the river discharge conditions (the hydrographs)</p> <p>07_Initial: files containing the initial conditions used to reduce the spin-up interval of the simulations.</p> <div> <div> <div> <div> <p>Delft3D source codes are downloadable from the Deltares model repository (https://oss. deltares.nl/web/delft3d).</p> </div> </div> </div> </div>
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.