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3,172 results for “Publications”
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Data and supplementary files of the publication: "What Goes Around Should Not Move Around: Immobilizing Microplastics as a New Approach for Analytical Ring Trials"
<p>Accompanying materials for the publication: <a href="https://doi.org/10.1021/acs.est.4c09427">https://doi.org/10.1021/acs.est.4c09427</a></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>
Data of the publication Hot ion implantation to create dense NV center ensembles in diamond
<p>Data of the publication published under the reference: M.W.<em> </em>Ngambeu Ngambou, Appl. Phys. Lett. 124, 134002 (2024).</p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Data for publication "The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zürich"
<p>Please see README.md for a description of this package. </p> <p>This work was funded by the European Union's Horizon 2020 research and innovation programme, grant agreement number 101037319, named Pilot Applications in Urban Landscapes - towards integrated city observatories for greenhouse gases (PAUL) and is known as ICOS Cities.</p> <p> </p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
Data for Publication - Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo
<p>Data used for the publication:</p> <p>"Synergies and Trade-offs between Robusta Yield, Carbon Stocks and Biodiversity across Coffee Systems in the DR Congo" - Ieben Broeckhoven, Jonas Depecker, Trésor Kasereka Muliwambene, Olivier Honnay, Roel Merckx and Bruno Verbist</p>
Experimental data related to the publication: "Phase Composition of AlTiNbMoV, AlTiNbTaZr and AlTiNbMoCr Refractory Complex Concentrated Alloys: A Correlation of Predictions and Experiment
<h2>EDS data</h2> <ul> <li>SPC and SPD files: EDAX Genesis raw data for EDS maps of as-sintered condition (Figures 1, 5 and 10).</li> <li><em>EDS_homogenized.h5</em> file: EDAX APEX raw data for maps (Figures 2, 6 and 11) and linescans (Figures 3, 7 and 12) of the homogenized condition</li> </ul> <h2>XRD data</h2> <p>ASCII tab-separated files for XRD diffractograms (Figures 4, 9 and 14). Contains measured intensity and calculated intensities for the displayed phases.</p> <h2>CALPHAD data</h2> <p>ASCII tab-separated files to recreate Figure 15 (resulting phase fractions only).</p> <p> </p>
Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"
<p>This dataset contains all the results and scenarios for this paper. Each region has two files in which you can find all the scenarios for the market design with and without the local energy market. The folders within these files contain the results for one scenario for a given share of PV, EVs, and heat pumps (HPs). The results folders can also be used to rerun the scenarios. To do so, place the respective scenario in the tool's scenario folder.</p> <p>USE CASE:<br>If you do not want to check all the files of the results or want to rerun the scenarios, use this version. If you want to download the relevant files for the analysis and figure creation of the paper, use the <a href="https://zenodo.org/records/13907329" target="_blank" rel="noopener">compact version</a>.</p> <p>TOOL:<br><a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a></p>
Compact version: Dataset for publication "Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares"
<p>This is the compact version of the results. They contain only the relevant files for the analysis and figure creation of the paper.</p> <p>USE CASE:<br>If you do not want to access every single file of the results or rerun the scenarios, you should use the compact version.</p> <p>TOOL:<br><a href="https://github.com/TUM-Doepfert/lemlab/tree/doepfert2024_lem">lemlab</a></p>
Dataset related to publication: Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures
<p>Dataset related to the publication:</p> <p><em>Giuseppe Emanuele Lio,</em><em> Sara Levorin</em><em>, Atakan Erdoğan</em><em>, Jérémy Werlé</em><em>, Alain J. Corso</em><em>, Luca </em><em>Schenato</em><em>, Diederik S. Wiersma</em><em>, Marco Santagiustina</em><em>, Lorenzo Pattelli</em><em>, Maria Guglielmina Pelizzo. </em><em>Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures, accepted and in publishing on International Journal of Thermophysics. </em>DOI: 10.1007/s10765-024-03439-8</p> <p>The repository contains the temperature, relative humidity, irradiance, sky temperature and net cooling power measurements in the file named "DATA_IJT_Nanoporous_Film_ Layer.txt" and the python script (Data_analizer.py) to analyze them as done in the paper.</p> <p> </p> <p>The repository also contain the Matlab workspace (IJT_data.mat)and code (Data_R_ATR_analyzer)to evaluate all the sample reflectance and FTIR measurements. Moreover, the extra measurements performed on the Celgard thin film are available in the workspace "Celgard_UV_VIS_NIR_T.mat" and "R_Celgard.mat". Moreover in Version 2 all the used data for Reflectance and Transmittance are also available in "txt" format for a simple and fast usage. </p> <p> </p> <h2>Notes (English)</h2> <div> <table> <tbody> <tr> <td> <p>This work was partly supported by</p> <ul> <li>European Project 21GRD03 PaRaMetriC, which received funding from the European Partnership on Metrology, co-financed by the European Union's Horizon Europe Research and Innovation Programme, and from the Participating States.</li> <li>European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended, and Ministerial Decree No. 1062 of August 10, 2021</li> </ul> </td> </tr> </tbody> </table> </div>
Set of images published in publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization"
<p>Figures of publication "Cleaning strategies for 3D-printed porous scaffolds used for bone regeneration fabricated via ceramic vat photopolymerization".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ceramint.2024.10.160" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ceramint.2024.10.160</span></span></a></p>
Open dataset for publication "Systematic Mapping Study on Requirements Engineering for Regulatory Compliance of Software Systems"
<p>This publication contains open dataset for the journal publication "Systematic Mapping Study on Requirements Engineering for Regulatory Compliance of Software Systems".</p> <p>The dataset contains the data extracted from 280 selected primary studies.</p> <p>The dataset includes the following data:</p> <ul> <li>study metadata (title, venue, publication year, authors, authors’ affiliation, abstract);</li> <li>challenges to regulatory compliance (direct excerpts from studies);</li> <li>categories of challenges to compliance;</li> <li>principles and practices (direct excerpts from text);</li> <li>categories of principles and practices;</li> <li>types of automation of principles and practices;</li> <li>involved stakeholders (direct excerpts from studies);</li> <li>categories of involved stakeholders;</li> <li>phase of the principle and practice life cycle for which involvement of stakeholders was considered;</li> <li>SDLC process areas covered by the study;</li> <li>regulations considered in the study;</li> <li>fields of regulations that were considered;</li> <li>domains of application that were considered;</li> <li>assessment of rigor and relevance of the study.</li> </ul>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
A vaccine-induced public antibody protects against SARS-CoV-2 and emerging variants
<p>These are the<strong> processed</strong> BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>. The <strong>raw</strong> sequence data are available on SRA under BioProjects <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>. </p> <p><strong>Summary</strong>: Bulk-sorted total plasmablasts and IgDlo enriched B cells from PBMCs and germinal centre B cells from lymph nodes from various timepoints after primary immunization from 22 BNT162b2 vaccinees who had no prior history of infection with SARS-CoV-2. </p> <p><strong>Metadata file</strong>: WU368_schmitz_et_al_immunity_2021_meta.tsv</p> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal center</li> <li>mAb = monoclonal antibody</li> </ul> <p><strong>BCR data file</strong>: WU368_schmitz_et_al_immunity_2021_bcr.tsv.gz</p> <p>In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs (including 2C08) first reported in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> that had been validated to be spike-binding. The mAbs are annotated as "mab" in the "seq_type" column.</p> <p><strong>Sequence data column description</strong></p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below.</p> <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>donor: vaccinee ID</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> </ul>
Softcite software mention extraction from the CORD-19 publications
<p><strong>Softcite software mention extraction from the CORD-19 publications </strong></p> <p>This dataset is the result of the extraction of software mentions from the set of publications of the CORD-19 corpus (<a href="https://allenai.org/data/cord-19">https://allenai.org/data/cord-19</a>) by the Softcite software recognizer (SciBERT-CRF fine-tuned model), see <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a>.</p> <p>The CORD-19 version used for this dataset is the one dated <strong>2021-07-26,</strong> using the <em>metadata.csv</em> file only. We re-harvested the PDF with <a href="https://github.com/kermitt2/article-dataset-builder">https://github.com/kermitt2/article-dataset-builder</a> in order to also extract coordinates of software mentions in the PDF and to take advantage of the latest version of GROBID to produce better full text extraction from PDF. We also harvested 61,230 full-texts more than the standard CORD-19 distribution.</p> <p>Note that this is the third version of this dataset (version 0.3.0). The previous Softcite software mention extraction from the CORD-19 was based on 2020-09-11 and 2021-03-22 versions. The new version cover a larger set of documents and is using an improved version of the extraction tools.</p> <p><strong>Data format </strong></p> <p>The extraction consists of 3 JSON files:</p> <p><strong>annotations.jsonl</strong> contains the individual software annotations including <em>software name</em> and possible attached attributes (<em>publisher</em>, <em>URL</em> and <em>version</em>). Each annotation is associated with coordinates expressed as bounding boxes in the original PDF. See <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the coordinate format.</p> <p>The context of citation is the sentence where the software name and its attributes are extracted. It is added to the JSON structure (field <em>context</em>), as well as the identifier of the document where the annotation belongs (field <em>document</em>, pointing to entries available in <em>documents.json</em>) and a list of bibliographical references attached to the software name (field <em>references</em>, pointing to entries available in <em>references.json</em>, with the used reference marker string). See <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a> for more details on the extracted attributes.</p> <p>If the software name was sucessfully disambiguated against WikiData ("entity linking"), it appears in the field <em>wikidataId</em> as Wikidata entity identifier and in the field <em>wikipediaExternalRef</em> as a Wikipedia PageID from the English Wikipedia. Entity linking is realized with <a href="https://github.com/kermitt2/entity-fishing">entity-fishing</a>.</p> <p><strong>documents.jsonl</strong> contains the metadata of the all the CORD-19 documents containing at least one software annotation. The metadata are given as a CrossRef JSON structure. The abstract should be included in the metadata most of the time, as well as some complements extracted by GROBID directly from the PDF. In addition, the size of the pages and the unique file path to the PDF can be found to allow annotations directly on the PDF (see <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the PDF annotation display mechanism).</p> <p><strong>references.jsonl </strong>contains the parsed reference entries associated to software mentions. These references are given in the field <em>tei</em> encoded in the XML TEI format of GROBID extraction. The extracted raw references have been matched against CrossRef to get a DOI and more complete metadata with <a href="https://github.com/kermitt2/biblio-glutton">biblio-glutton</a>.</p> <p><strong>Statistics</strong></p> <p>CORD-19 version: 2021-07-26</p> <p>- total Open Access full texts: 296,686<br> - with at least one software mention: 115,073</p> <p>- total software name annotations: 652,518<br> - with linked Wikidata ID: 231,599</p> <p>- associated field <br> - publisher: 107,421<br> - version: 188,724<br> - URL: 59,366<br> - references: 230,145</p> <p>- associated bibliographical references: 92,573<br> - references with matched DOI: 49,350<br> - references with matched PMID: 32,895<br> - references with matched PMC ID: 18,741</p> <p><strong>License and acknowledgements</strong></p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International License.</p> <p>We thank the Alfred P. Sloan Foundation and of the Gordon and Betty Moore Foundation for supporting this work.</p>
Reproduction package for the publication 'New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX'
<p>The following files can be used to reproduce the Figures and data from the paper <strong>New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX </strong>by L. Štofanová, J. Kaastra, M. Mehdipour, and J. de Plaa accepted to be publish in Section 12. Atomic, molecular, and nuclear data of Astronomy and Astrophysics (acceptance date - 27/06/2021).</p> <p> </p> <p>Note: version 2 is the most updated version (change in Fig.7).</p>
Age-dependent extreme event exposure - data accompanying journal publication
<p>This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science</p> <p> </p> <p>Input fields:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/isimip.zip">isimip.zip</a>: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/GMT_50pc_manualoutput_4pathways.xlsx">GMT_50pc_manualoutput_4pathways.xlsx</a>: Global mean temperature anomaly trajectories from the IPCC SR15</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/wcde_data.xlsx">wcde_data.xlsx</a>: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx">WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx</a>: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme</p> <p> </p> <p>Intermediate files *only use if you're interested in reproducing the results*:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/workspaces.zip">workspaces.zip</a>: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis.</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_isimip.mat">mw_isimip.mat</a>: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_countries.mat">mw_countries.mat</a>: information on the countries used in the analysis (e.g. border polygon coordinates)</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure.mat">mw_exposure.mat</a>: age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic.mat">mw_exposure_pic.mat</a>: pre-industrial control age-dependent exposure computed from the ISIMIP and population data</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_exposure_pic_coldwaves.mat">mw_exposure_pic_coldwaves.mat</a>: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data</p> <p> </p> <p> </p> <p>Output of the analysis:</p> <p>- <a href="https://zenodo.org/api/files/9b674428-38e0-4395-a1c0-61b23e9ce3dc/mw_output.mat">mw_output.mat</a>: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.</p> <p> </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.