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3,121 results for “Resources”
Dataset for Physician Preparedness for Resource Allocation Decisions Under Pandemic Conditions: A Cross-Sectional Survey of Canadian Physicians, April 2020
<p>Survey responses. </p>
German-Swiss survey on the use of (digital) language resources
<p>In this document the raw data of an online survey (n=255) on the use of (digital) language resources in German-speaking Switzerland is made available to the public.The corresponding codes are described in more detail in the codebook also published on Zenodo (cf. 10.5281/zenodo.4314348). In addition, the website of the project "Digital Language Resources - Empirical Analyses and Perspectives" (cf. https://www.ds.uzh.ch/de/projekte/digitale-sprachressourcen) offers a detailed presentation of the results elaborated on the basis of this data. The study complements the Europe-wide survey conducted by the European Network of e-Lexicography (ENeL) and was carried out in close cooperation with the Leibniz-Institute for the German Language (IDS) in Mannheim. </p> <p> </p> <p>T</p>
Factors associated with emotional exhaustion in healthcare professionals involved in the COVID-19 pandemic: an application of the job demands-resources model
<p>Dataset from Barello S, Caruso R, Palamenghi L, Nania T, Dellafiore F, Bonetti L, Silenzi A, Marotta C, Graffigna G. Factors associated with emotional exhaustion in healthcare professionals involved in the COVID-19 pandemic: an application of the job demands-resources model. Int Arch Occup Environ Health. 2021 Nov;94(8):1751-1761. doi: 10.1007/s00420-021-01669-z. Epub 2021 Mar 3. PMID: 33660030; PMCID: PMC7928172.</p> <p>Abstract</p> <p><strong>Purpose: </strong>The purpose of the present cross-sectional study is to investigate the role of perceived COVID-19-related organizational demands and threats in predicting emotional exhaustion, and the role of organizational support in reducing the negative influence of perceived COVID-19 work-related stressors on burnout. Moreover, the present study aims to add to the understanding of the role of personal resources in the Job Demands-Resources model (JD-R) by examining whether personal resources-such as the professionals' orientation towards patient engagement-may also strengthen the impact of job resources and mitigate the impact of job demands.</p> <p><strong>Methods: </strong>This cross-sectional study involved 532 healthcare professionals working during the COVID-19 pandemic in Italy. It adopted the Job-Demands-Resource Model to study the determinants of professional's burnout. An integrative model describing how increasing job demands experienced by this specific population are related to burnout and in particular to emotional exhaustion symptoms was developed.</p> <p><strong>Results: </strong>The results of the logistic regression models provided strong support for the proposed model, as both Job Demands and Resources are significant predictors (OR = 2.359 and 0.563 respectively, with p < 0.001). Moreover, healthcare professionals' orientation towards patient engagement appears as a significant moderator of this relationship, as it reduces Demands' effect (OR = 1.188) and increases Resources' effect (OR = 0.501).</p> <p><strong>Conclusions: </strong>These findings integrate previous findings on the JD-R Model and suggest the relevance of personal resources and of relational factors in affecting professionals' experience of burnout.</p>
Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.
<p>Data and generating files for the manuscript "A Multi-Model Analysis of Solute Plume Behavior in a Synthetic Braided-River Deposit", submitted to Water Resources Research, August 2018.</p> <p>This zipped folder contains the codes and data generated for the manuscript. The main routine for generating the ensembles is fidelity/run_fidelity.py. The folders 'dtgeostats', 'flowtrans', and 'hyvr' contain utilities for generating parameter fields and running flow-and-transport simulations. Note that the codes and data are provided as-is and relative pathways, etc. in the code may not function correctly. The data can be found in the fidelity/runfiles/braid005 directory and includes the outputs for the synthetic virtual reality (fidelity/runfiles/braid005/braid_vr) and the following model ensembles: object-based with no conditioning (fidelity/runfiles/braid005/braid_1a), object-based with soft conditioning (fidelity/runfiles/braid005/braid_1b), MPS (fidelity/runfiles/braid005/braid_2a/), isotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/isn/), and anisotropic multi-Gaussian (fidelity/runfiles/braid005/braid_3/ann/) .</p>
e-resources 2022
<p>Photos 2021-2022</p>
Data for the publication: Struggles for democracy: strategies and resources of initiatives for non-citizen voting rights at local level in Europe
<p>The uploaded files present research material of the project: Initiatives for Non-Citizen Voting Rights in Three European Countries (nccr - on the move, Switzerland) and of the publication: </p> <p>Sontag, K., Herzog, M. & Lässer, S. Struggles for democracy: strategies and resources of initiatives for non-citizen voting rights at local levels in Europe. <em>CMS</em> 10, 17 (2022). https://doi.org/10.1186/s40878-022-00286-0</p> <p>The dataset contains survey questionnaires, links to webpages, metadata of interviews and a user manual.</p>
CLARA Knowledge Graph of licensed educational resources (Singleton properties)
<p><span>/!\</span> This deposit is deprecated; a more complete version of the deposit can be found here: <a href="https://zenodo.org/records/8403142">8403142</a>. <span>/!\</span></p> <p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). The files used as inputs can be found <a href="http://10.5281/zenodo.8107150">here</a>. That pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Content</strong><br>The present Knowledge Graph contains information about 45K educational resources and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the educational resource,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores, a PageRank score and a cosinus score (that were <strong>not</strong> calculated on our graph but during the X5GON project)</p> <p>A particularity of the Knowledge Graph is its heavy use of RDF reification, across large multi-valued properties.<br>Other versions of the same deposit can be found using different reification models:</p> <ul> <li><a href="https://zenodo.org/record/8108856">Standard reification</a></li> <li><a href="https://zenodo.org/record/8108948">Named graphs</a></li> <li><a href="https://zenodo.org/record/8108971">RDF-star</a></li> </ul> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The deposit contains five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nt</strong> - Those contain the educational resources and their information using singleton properties.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul>
CLARA Knowledge Graph of licensed educational resources (RDF-star)
<p><span>/!\</span> This deposit is deprecated; a more complete version of the deposit can be found here: <a href="https://zenodo.org/records/8403142">8403142</a>. <span>/!\</span></p> <p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://ceur-ws.org/Vol-2980/paper374.pdf">RML-star</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). The files used as inputs can be found <a href="http://10.5281/zenodo.8107150">here</a>. That pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Content</strong><br>The present Knowledge Graph contains information about 45K educational resources and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the educational resource,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores, a PageRank score and a cosinus score (that were <strong>not</strong> calculated on our graph but during the X5GON project)</p> <p>A particularity of the Knowledge Graph is its heavy use of RDF reification, across large multi-valued properties.<br>Other versions of the same deposit can be found using different reification models:</p> <ul> <li><a href="https://zenodo.org/record/8108856">Standard reification</a></li> <li><a href="https://zenodo.org/record/8108948">Named graphs</a></li> <li><a href="https://zenodo.org/record/8108963">Singleton properties</a></li> </ul> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The deposit contains five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].ttl</strong> - Those contain the educational resources and their information using RDF-star.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul>
CLARA Knowledge Graph of licensed educational resources (Named graphs)
<p><span>/!\</span> This deposit is deprecated; a more complete version of the deposit can be found here: <a href="https://zenodo.org/records/8403142">8403142</a>. <span>/!\</span></p> <p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). The files used as inputs can be found <a href="http://10.5281/zenodo.8107150">here</a>. That pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Content</strong><br>The present Knowledge Graph contains information about 45K educational resources and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the educational resource,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores, a PageRank score and a cosinus score (that were <strong>not</strong> calculated on our graph but during the X5GON project)</p> <p>A particularity of the Knowledge Graph is its heavy use of RDF reification, across large multi-valued properties.<br>Other versions of the same deposit can be found using different reification models:</p> <ul> <li><a href="https://zenodo.org/record/8108856">Standard reification</a></li> <li><a href="https://zenodo.org/record/8108963">Singleton properties</a></li> <li><a href="https://zenodo.org/record/8108971">RDF-star</a></li> </ul> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The deposit contains five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nq</strong> - Those contain the educational resources and their information using named graphs.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul>
CLARA Knowledge Graph of licensed educational resources (Standard reification)
<p><span>/!\</span> This deposit is deprecated; a more complete version of the deposit can be found here: <a href="https://zenodo.org/records/8403142">8403142</a>. <span>/!\</span></p> <p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). The files used as inputs can be found <a href="http://10.5281/zenodo.8107150">here</a>. That pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Content</strong><br>The present Knowledge Graph contains information about 45K educational resources and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the educational resource,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores, a PageRank score and a cosinus score (that were <strong>not</strong> calculated on our graph but during the X5GON project)</p> <p>A particularity of the Knowledge Graph is its heavy use of RDF reification, across large multi-valued properties.<br>Other versions of the same deposit can be found using different reification models:</p> <ul> <li><a href="https://zenodo.org/record/8108948">Named graphs</a></li> <li><a href="https://zenodo.org/record/8108963">Singleton properties</a></li> <li><a href="https://zenodo.org/record/8108971">RDF-star</a></li> </ul> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The deposit contains five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nt</strong> - Those contain the educational resources and their information using standard reification.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul>
Mutant Mouse Regional Resource Center (MMRRC) Research
GEO Series GSE1920. Mus musculus. 358 samples. Type: Expression profiling by array.
LRO DLRE 5 POLAR RESOURCE PRODUCT
This data set consists of the Diviner Lunar Radiometer Experiment Polar Resource Products also known as PRPs. The DLRE is a surface pushbroom mapper that measures emitted thermal radiation and reflected solar radiation from the surface of the moon. Two Diviner solar channels measure 0.3-3 micrometers reflected solar radiation. Three Diviner channels near 8 micrometers classify regolith mineralogy by mapping the location of the Christiansen feature. The remaining four Diviner channels measure surface temperature in four spectral bands ranging from 12.5 micrometers to beyond 200 micrometers.
Hurricane Resource Reel
This Reel Includes the Following Sections TRT 50:10 Hurricane Overviews 1:02; Hurricane Arthur 15:07; Cyclone Pam 19:48; Typhoon Hagupit 21:27; Hurricane Bertha 22:03; Hurricanes Iselle and Julio 23:15; September 2014 Hurricane Alley 25:07; Satellite Beauty Passes 28:31; Hurricane Katrina 36:32; Global Portrait of Precipitation 42:00; Typhoon Halong 42:36; Typhoon Maysak 43:13; Superstorm Sandy 44:21; Hurricanes Fay and Gonzalo 45:29; RapidScat 46:12; CYGNSS 49:16 Super(s): NASA; Center Contact: Rob Gutro 301-286-4044; HQ Contact: Steve Cole 202-358-0918.
Rapid growth of new atmospheric particles by nitric acid and ammonia condensation: data resources
<p>Data presented in the manuscript "Rapid growth of new atmospheric particles by nitric acid and ammonia condensation" currently in review.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration</p>
Arbitration of I/O Forwarding Resources
<p>This repository contains datasets, source-codes, and parsing scripts from our paper.</p>
Energy metals SI Resources
<p>Full calculation and data for modelling supply constraints of energy metals in a 100 % renewable energy system.</p> <p>Results published in Resources: Enough metals? Resource Constraints to Supply a Fully Renewable Energy System</p>
Inter-annual precipitation variability alters the effects of soil resource enrichment and mycorrhizal suppression on plant communities in desert steppe
<p><strong>Questions:</strong> In desert steppes, plant community productivity and species composition are primarily co-limited by soil water and nitrogen availability. However, how the addition of resources and arbuscular mycorrhizal fungi interactively influence plant community performance has not been investigated in detail.</p> <p><strong>Location: </strong>Desert steppe, China.</p> <p><strong>Methods:</strong> We performed a field study to examine the effects of levels of increased water, nitrogen and arbuscular mycorrhizal (AM) fungal suppression on plant communities in 2 year with contrasting inter-annual precipitation variability in desert steppe.</p> <p><strong>Results:</strong> We found that inter-annual precipitation variability altered plant community composition and productivity in responses to water and nitrogen addition and mycorrhizal suppression. AM fungal suppression had few effects on aboveground plant productivity, whereas the primary effects of water and nitrogen addition were significant across all two years. The addition of water and nitrogen altered plant community composition and productivity primarily by increasing the abundance and biomass of annual species only in normal year (2019), but decreasing the abundance of perennial grasses regardless of the presence of AM fungi across two years with contrasting precipitation conditions. Moreover, the addition of water alone slightly increased the species richness and Shannon diversity compared with the control.</p> <p><strong>Conclusions:</strong> Our findings highlight the importance of inter-annual precipitation variability rather than that of soil resource enrichment and plant–arbuscular mycorrhizal fungi interactions in plant community composition within the desert steppe.</p>
Resilience Resources, Coping, and Health Outcomes in College Students during the Covid-19 Pandemic
<p>The current study tests coping behaviors as mediators of associations between resilience resources and somatization, depression, and anxiety symptoms in undergraduates during the Covid-19 pandemic. Participants (n=193) completed an online survey assessing resilience resources, coping, and symptoms in late 2020. The data set includes variables tested in three parallel mediation models. </p> <p>RRS=Resilience Resources Scale</p> <p>copePR=Coping through Positive Reinterpretation and Growth</p> <p>copeES=Coping through Seeking Emotional Support</p> <p>copeSU=Coping through Substance Use</p> <p>phq = Somatic Symptoms (PHQ-15)</p> <p>dpsx =Depressive Symptoms (PROMIS)</p> <p>anxsx =Anxiety Symptoms (PROMIS)</p> <p> </p>
Is Over-taking Immoral? ——Taking and Donating in Resource Dilemma with no damage threshold
<p>先前的研究表明,社会价值取向和反馈会影响涉及环境问题的合作行为,但它们为环境设定了明确的承载阈值,导致场景扭曲。为了更好地反映现实,我们模拟了鱼缸中的自然环境,并创造了一个阈值未知的水资源困境。结果表明,社会价值取向不能预测资源获取,但与捐赠资源修复环境显著相关。环境破坏后的内疚与社会价值取向或资源获取无关,而是预测捐赠。反馈认为研究员捐赠更多(研究1)或自己是环境破坏的主要原因(研究2)增加了捐赠。此外,研究2发现生命价值减少了资源占用和增加捐赠。我们的研究结果表明,资源获取不能反映社会价值观取向,但捐赠可以。在现实场景中,服用更多的人不应该因为无法获得伤害阈值而受到严厉批评。</p> <p> </p> <p>抽象148 / 2</p>
Specimen Resource Locator Repositories
The SRL Repositories consist of 416 biospecimen collections from 44 resources.
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.