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3,172
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3,172 results for “Publications”
Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.
<p>Dataset for the publication titlted "A computational mechanics model for producing molecular assembly using molecularly woven pantographs" in the journal Cell Reports Physical Science, authored by Byeonghwa Goh and Joonmyung Choi.</p>
Data underlying the publication: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago."
<p><strong>CAR36 dataset</strong></p><p>These data correspond to the <strong>1-year (2019)</strong> simulation from the regional ocean system CAR36. These <strong>daily hindcasts</strong> have been used in the study presented in the paper submitted in GMD editor and entitled: "CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean Archipelago", where the CAR36 system is fully described.</p><p><br>The uploaded files are in <strong>netcdf</strong> format:</p><ul><li><i>CAR36_daily_SSH_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Height </strong></li><li><i>CAR36_daily_SST_20190102-20191224.nc </i>= 1-year daily hindcasts of <strong>Sea Surface Temperature</strong></li><li><i>CAR36_daily_SSU_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (zonal component)</strong></li><li><i>CAR36_daily_SSV_20190102-20191224.nc</i> = 1-year daily hindcasts of <strong>Sea Surface Current Speed (meridian component)</strong></li></ul><p>All data are projected on the native model tripolar<strong> ORCA grid</strong> <strong>in 1/36° </strong>horizontal resolution.</p><p>NB: In order to filter (in a 1st order) the semi-diurnal tidal signal (with a period of 12h30), the daily mean corresponds to a 25h-average. </p><p><strong>CAR36 software</strong></p><p>The NEMO_CAR36.tar file gathers the <strong>NEMO code configuration</strong> of the CAR36 model. This code follows the same license than NEMO one : <strong>CeCILL</strong>. A file named "License_CeCILL.txt" reminds the details of this license in the NEMO_CAR36.tar file.<br><br>NB.: This model have been renamed CAR36 (English acronym) for the paper instead of ARCAN36 (French initial acronym). In the provided NEMO code, the name ARCAN36 is still used. </p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
Dataset from publication "Predicting context-sensitive urban green space quality to support urban green infrastructure planning"
<p><strong>Data Description:</strong></p><p>This dataset presents the spatial outcome of an analysis modelling perceived green space quality across the city of Espoo, Finland. The analysis relies on data gathered through the My Espoo on the Map survey (<i>Mun Espoo kartalla</i>) in the autumn of 2020 as part of the NordForsk-funded research project NORDGREEN. A comprehensive account of the analytical process and potential applications of the dataset is available in the associated publication, "<i>Predicting context-sensitive urban green space quality to support urban green infrastructure planning</i>" (open access: <a href="https://doi.org/10.1016/j.landurbplan.2023.104952">https://doi.org/10.1016/j.landurbplan.2023.104952</a>).</p><p><strong>Data Processing:</strong></p><p>This dataset results from an analysis that integrates both primary and secondary sources of geospatial data. The primary data were collected with an online public participation GIS (PPGIS) survey directed for the adult inhabitants of Espoo. The data collection took place in September-October 2020 and was executed in collaboration with Aalto University and the City of Espoo. For a detailed overview of the data collection process, please refer to the related publication.</p><p><strong>Data characteristics:</strong></p><p>Format: Shapefile (50m x 50m grid)</p><p>Geographical area: Espoo, Finland</p><p>Spatial reference: EUREF FIN TM35FIN</p><p>Note: Only grid cells intersecting with greenspace have been included in the dataset. For the employed definition of green areas, please consult the related publication.</p><p><strong>Data attributes and their descriptions:</strong></p><p><strong>"</strong><i>P_PROB"</i>: Probability (P), positive perceived quality</p><p><i>"N_PROB"</i>: Probability (P), negative perceived quality</p><p><strong>Funding: </strong></p><p>This research was funded by NordForsk, Sustainable Urban Development and Smart Cities Programme, Project Smart Planning for Healthy and Green and Nordic Cities – NORDGREEN, under Grant Number: 95322.</p>
Public funding accountability: a linked open data-based methodology for analysing the scientific productivity and influence of funded projects. Dataset
<p>Tables containing the information about the projects funded by the Spanish AEI and publications acknowledging funding from Funder Registry. Both datasets where used in our publication: <em>Public funding accountability: a linked open data-based methodology for analysing the scientific productivity and influence of funded projects</em>.</p>
Source data for the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator"
<p>This repository contains data reported in the publication "Longitudinal coupling between a Si/SiGe double quantum dot and an off-chip TiN resonator."</p>
Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
<p><span>Multiple genes encoding family A DNA polymerases (famA DNAPs), which are evolutionary relatives of DNA polymerase </span><span>I</span><span> (Pol</span><span>I</span><span>) in bacteria and phages, have been found in eukaryotic genomes, and many of these proteins are used mainly in organelles. Among members of the phylum Euglenozoa, distinct types of famA DNAP, Pol</span><span>I</span><span>A, Pol</span><span>I</span><span>BCD+, POP, and eugPolA, have been found. It is intriguing how the suite of famA DNAPs had been established during the evolution of Euglenozoa, but the DNAP data have not been sampled from the taxa that sufficiently represent the diversity of this phylum. In particular, little sequence data were available for basal branching species in Euglenozoa until recently. Thanks to the single-cell transcriptome data from symbiontids and phagotrophic euglenids, we have an opportunity to cover the "hole" in the repertory of famA DNAPs in the deep branches in Euglenozoa. The current study identified 16 new famA DNAP sequences in the transcriptome data from 33 phagotrophic euglenids and two symbiontids, respectively. Based on the new famA DNAP sequences, the updated diversity and evolution of famA DNAPs in Euglenozoa are discussed.</span></p>
Public data for nonlinear optical computational histology
<p>Experimental datasets for training and testing nonlinear optical computational histology (NOCH), which include label-free nonlinear optical data: stimulated Raman scattering (SRS) images of human brain tumors and multiphoton (MP) images of human ovarian cancers, and the corresponding H&E slices. The <a href="https://github.com/shenblin/NOCH">contrastive deep learning framework</a> can generate diagnostic quality H&E slides comparable to conventional histopathology. </p> <p>If you find this work useful in your research, please consider citing the paper:</p> <p><a href="https://doi.org/10.1002/advs.202308630">B. Shen, Z. Li, Y. Pan, Y. Guo, Z. Yin, R. Hu, J. Qu, L. Liu, Noninvasive Nonlinear Optical Computational Histology. Adv. Sci. 2023, 2308630.</a></p>
Data and Scripts for Publication: Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
<p>Data and scripts for the publication titled Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy.</p> <p>Article available at <a href="https://doi.org/10.1088/2399-6528/ad1f73">10.1088/2399-6528/ad1f73</a></p>
Knowledge of Primary Health Care Nurses at Selected Public Institutions Regarding Prescription of Antihypertensive Drugs
<p>Hypertension has been among the most studied topics of the previous century and has been one of the most significant comorbidities contributing to the development of stroke, myocardial infarction, heart failure, and renal failure. In the mist of that, there is limited data on nurses’ knowledge regarding prescription of antihypertensive drugs in Lesotho. The aim of this study was to assess primary health care nurses’ knowledge regarding prescription of antihypertensive drugs. Quantitative descriptive cross-sectional study of primary health care nurses recruited by purposive sampling was conducted. An electronic questionnaire was used to collect data and data were analyzed using descriptive statistics. Most participants were females and had attained diploma in general nursing and midwifery. A proportion of 26% fully knew the steps to be followed for diagnosing hypertension and only 23% felt highly confident to initiate patients on antihypertensive drugs. Moreover, knowledge on mechanism of action of antihypertensive drugs was poor and less than half only knew antihypertensive drug of choice when there is comorbidity. The study identified that nurses have knowledge deficit regarding prescription of antihypertensive drugs. There is a need for interventions to improve nurses’ knowledge so as to curb hypertension burden. </p>
Divergent Characteristics of Biomedical Research across Publication Types: A Quantitative Analysis on the Aging-related Research
<p>Attached in pdf here.</p>
Amsterdam Public Trash can 1932 Peter Marnette
Cast Iron [Public Trash can](https://items.amsterdamse-school.nl/details/objects/1505) in Amsterdam School style designed bij architect [Peter Marnette](http://items.amsterdamse-school.nl/details/persons/175) This one was photographed in the garden of Museum het Schip and is part of their [street furniture collection](https://www.hetschip.nl/amsterdamse-school/collectie/collectie-straatmeubilair) The **SR** means Stads Reiniging that translates to: municipal cleaners* Source: Objaverse 1.0 / Sketchfab
Public Tap
Szentendre, Hungary Made With Nikon D3100 & Nikkor 18-55 This is the Low Poly Version. High Poly models and materials are avaiable for patrons via https://www.patreon.com/gerialkot Source: Objaverse 1.0 / Sketchfab
The Paradox of Innovation Non-Disclosure: Evidence from Licensing Contracts - Miscellaneous Public Data
<p>Various datasets</p> <p> </p> <ol> <li>Hoberg Phillips data library</li> <li>Kempf Manconi Spalt distraction data</li> </ol> <p> </p> <p> </p> <p><span><strong>All links in our collection</strong></span></p> <p>EDGAR Log Batch 1: <a href="../records/10403941">https://zenodo.org/records/10403941</a></p> <p>EDGAR Log Batch 2: <a href="../records/10404834">https://zenodo.org/records/10404834</a></p> <p>EDGAR Log Batch 3: <a href="../records/10404840">https://zenodo.org/records/10404840</a></p> <p>EDGAR Log Batch 4: <a href="../records/10404846">https://zenodo.org/records/10404846</a></p> <p>EDGAR Log Batch 5: <a href="../records/10404858">https://zenodo.org/records/10404858</a></p> <p>EDGAR Log Batch 6: https://zenodo.org/records/10408818</p> <p>EDGAR Log Batch 7 : <a href="../records/10408820">https://zenodo.org/records/10408820</a></p> <p>Patent data: <a href="../records/10426559">https://zenodo.org/records/10426559</a></p> <p>Miscelleanous data: <a href="../records/10440397">https://zenodo.org/records/10440397</a></p> <p> </p>
Public phone / telefono publico
telefono publico de monedas modelo crosley, inventado en 1889 por el norteamericano William Gray. El primer aparato público estuvo a disposición de la gente en un banco de la ciudad de Hartford, estado de Connecticut. Source: Objaverse 1.0 / Sketchfab
Bronze public fountain in Bruges, Belgium
Bronze figurehead what originally functioned as a public fountain. Bruges (Brugge), Belgium. Rendered from 175 images, captured by a Fujifilm XT-2 camera. Mesh simplified in ZBrush. Source: Objaverse 1.0 / Sketchfab
Pompeii - Public Water Fountain 19
This public water fountain was scanned as part of the 2022 Pompeii Water and Sanitation Project field school run by the University of Missouri. The fountain is situated between VI.16.3 and VI.16.4 and is made of grey peperino slabs. The relief on the spout stone is of an animal, either a jackal or panther. More photos can be seen here: https://pompeiiinpictures.com/pompeiiinpictures/Fountains/Fountain%2061603.htm The model was generated in Agisoft Metashape from 300 photos taken using a Nikon Z5. Photo alignment was run on high, as was model generation - model has been decimated for export, and made manifold for 3D printing. Source: Objaverse 1.0 / Sketchfab
FIGURE 4 in Publication and impact: a bibliometric survey of Megataxa 1-10
FIGURE 4. Distribution of authors among countries in Megataxa 1‒10.
Supplementary material for the publication: Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
Open the record for dataset details and reuse information.
PARSEC D(DO)MP. Project tracksheets: scholarly publications, non peer-reviewed digital outputs, dataset log, and software log.
<p>This project outcome is allied to the Data and Digital Output Management Plan for the project (Stall et al. 2023). This resource contains templates for others to use when tracking their project outcomes and usages, accompanied by filled worksheets for the PARSEC project up until the date of version 2 (30 December 2023).</p> <p>The material provided is available as examplars from the PARSEC project and for use by others (excel templates) for the following categories:</p> <ol> <li>Scholarly Publications </li> <li>Non Peer-Reviewed Digital Outputs (to include Datasets, Software, Conferences, Posters, Presentations, Training Materials, Workshop Materials, etc.)</li> <li>Datasets - working log of datasets reviewed for use by the project </li> <li>Software - working log for software reviewed by the project (to include Models, Notebooks, Workflow, etc.)</li> </ol> <p>V2 contains an update for non peer-reviewed digital outputs (2a) containing new and discovered outputs.</p> <p>Stall, Shelley, Specht, Alison, Corrêa, Pedro Luiz Pizzigatti, David, Romain, Edmunds, Rorie, Mabile, Laurence, Machicao, Jeaneth, Miyairi, Nobuko, Murayama, Yasuhiro, O'Brien, Margaret, Wyborn, Lesley, & Vellenich, Danton Ferreira. (2023). PARSEC Data and Digital Output Management Plan and Workbook. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.3891426">10.5281/zenodo.3891426</a>.</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.