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Validation of the Spanish Sexual Risk Behaviour Scale (SRBS): Psychosocial factors influencing sexual health risk-taking among adolescents, youth, and future professionals
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Data sets of the article Validation of the Health Protective Sexual Communication Scale (HPSC) in British and Spanish young adults.
<p>Data sets of the article <em>Validation of the Health Protective Sexual Communication Scale (HPSC) in British and Spanish young adults. </em></p> <p>SPSS data sets used to perform Exploratory Factor Anlaysis (EFA) and Confirmatory Factor Analysis (CFA) in Spain and the United Kingdom.</p> <p>SPSS AMOS data sets to perform CFA in Spain and the United Kingdom.</p>
datet related to article "The Pre-Lumbar puncture Intracranial Hypertension Scale (PLIHS): A practical scale to identify subjects with normal cerebrospinal fluid pressure in the management of idiopathic intracranial hypertension"
<p>The dataset contains the variables used to validata the PLIHS scale described in the manuscript.</p>
Nursing self-efficacy for oral anticoagulant therapy management: Development and initial validation of a theory-grounded scale
<p>Dataset from Magon A, Arrigoni C, Fava A, Pittella F, Villa G, Dellafiore F, Conte G, Caruso R. Nursing self-efficacy for oral anticoagulant therapy management: Development and initial validation of a theory-grounded scale. Appl Nurs Res. 2021 Jun;59:151428. doi: 10.1016/j.apnr.2021.151428. Epub 2021 Mar 23. PMID: 33947515.</p> <p>Abstract</p> <p><strong>Aim: </strong>This study aimed to develop and validate a nursing self-efficacy scale for OAC management (SE-OAM).</p> <p><strong>Background: </strong>Oral anticoagulant therapy (OAC) requires specific nursing competencies. Given that self-efficacy acts as a proxy assessment of nursing competence, its measurement is pivotal for addressing educational programs to enhance nursing competence in managing OAC. Thus far, the measurement of self-efficacy in OAC is undermined by the unavailability of valid and reliable tools.</p> <p><strong>Methods: </strong>A multi-method and multi-phase design was adopted: Phase one was a methodological study encompassing developmental tasks for generating items. Phase two comprised the validation process for determining the content validity, construct and concurrent validity, and internal consistency through two cross-sectional data collections.</p> <p><strong>Results: </strong>In total, 190 nurses were enrolled for determining the psychometric structure of the SE-OAM through an exploratory approach, and 345 nurses were subsequently enrolled to corroborate its most plausible factor structure derived from the exploratory analysis. The SE-OAM showed evidence of face and content validity, adequate construct, concurrent validity, good internal consistency, and stability. The final version of the scale encompassed 21 items kept by five domains: clinical management, care management, education, clinical monitoring, and care monitoring.</p> <p><strong>Conclusions: </strong>The SE-OAM showed evidence of initial validity and reliability, fulfilling a current gap in the availability of tools for measuring nursing self-efficacy in managing OAC. SE-OAM could be strategic for performing research to improve the quality of OAC management by enhancing nursing self-efficacy.</p>
Research Artefact: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types
<p>This is a research artefact for the paper: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types. This artefact is a repository consisting of the collected dataset including 447,542 React-related SO question posts and 384 representative samples that were obtained randomly. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
Research Artefact: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types
<p>This is a research artefact for the paper: A Large-Scale Study of React-Related Questions in Stack Overflow: Frequency, Popularity, and Types. This artefact is a repository consisting of the collected dataset including 447,542 React-related SO question posts and 384 representative samples that were obtained randomly. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.</p>
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)
<p>This data set contains information on the agricultural land use in Germany for the year 2020.<br> The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>.<br> The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>.</p> <p>Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.</p> <p> </p> <p>Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.</p> <table> <tbody> <tr> <td> <p><strong>New class code (V2) </strong></p> </td> <td> <p><strong>Class name (V2)</strong></p> </td> <td> <p><strong>Class code (V1)</strong></p> </td> <td> <p><strong>Class name (V1)</strong></p> </td> </tr> <tr> <td> <p>1101</p> </td> <td> <p>Winter wheat</p> </td> <td> <p>31</p> </td> <td> <p>Winter wheat</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>34</p> </td> <td> <p>Other winter cereals</p> </td> </tr> <tr> <td> <p>1102</p> </td> <td> <p>Winter barley</p> </td> <td> <p>33</p> </td> <td> <p>Winter barley</p> </td> </tr> <tr> <td> <p>1103</p> </td> <td> <p>Winter rye</p> </td> <td> <p>32</p> </td> <td> <p>Winter rye</p> </td> </tr> <tr> <td> <p>1201</p> </td> <td> <p>Spring barley</p> </td> <td> <p>41</p> </td> <td> <p>Spring barley</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>43</p> </td> <td> <p>Other spring cereals</p> </td> </tr> <tr> <td> <p>1202</p> </td> <td> <p>Oat</p> </td> <td> <p>42</p> </td> <td> <p>Spring oat</p> </td> </tr> <tr> <td> <p>1300</p> </td> <td> <p>Maize</p> </td> <td> <p>91</p> </td> <td> <p>Maize (silage)</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>92</p> </td> <td> <p>Maize (grain)</p> </td> </tr> <tr> <td> <p>1401</p> </td> <td> <p>Potatoe</p> </td> <td> <p>100</p> </td> <td> <p>Potatoe</p> </td> </tr> <tr> <td> <p>1402</p> </td> <td> <p>Sugar beet</p> </td> <td> <p>80</p> </td> <td> <p>Sugar beet</p> </td> </tr> <tr> <td> <p>1501</p> </td> <td> <p>Rapeseed</p> </td> <td> <p>50</p> </td> <td> <p>Winter rapeseed</p> </td> </tr> <tr> <td> <p>1502</p> </td> <td> <p>Sunflower</p> </td> <td> <p>70</p> </td> <td> <p>Sunflower</p> </td> </tr> <tr> <td> <p>1611</p> </td> <td> <p>Peas</p> </td> <td> <p>60</p> </td> <td> <p>Legume</p> </td> </tr> <tr> <td> <p>1612</p> </td> <td> <p>Broad beans</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1613</p> </td> <td> <p>Lupine</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1614</p> </td> <td> <p>Soy</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1603</p> </td> <td> <p>Vegetables</p> </td> <td> <p>120</p> </td> <td> <p>Strawberry</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>130</p> </td> <td> <p>Asparagus</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>140</p> </td> <td> <p>Onion</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>181</p> </td> <td> <p>Carrot</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>182</p> </td> <td> <p>Other leafy vegetables</p> </td> </tr> <tr> <td> <p>1602</p> </td> <td> <p>Cultivated grassland</p> </td> <td> <p>10</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>200</p> </td> <td> <p>Permanent grassland</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3003</p> </td> <td> <p>Fallow land</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3001</p> </td> <td> <p>Small woody features</p> </td> <td> <p>555</p> </td> <td> <p>Small woody features</p> </td> </tr> <tr> <td> <p>3002</p> </td> <td> <p>Other areas</p> </td> <td> <p>999</p> </td> <td> <p>Other agricultural areas</p> </td> </tr> <tr> <td> <p>4001</p> </td> <td> <p>Grapevine</p> </td> <td> <p>110</p> </td> <td> <p>Grapevine</p> </td> </tr> <tr> <td> <p>4002</p> </td> <td> <p>Hops</p> </td> <td> <p>150</p> </td> <td> <p>Hops</p> </td> </tr> <tr> <td> <p>4003</p> </td> <td> <p>Orchard</p> </td> <td> <p>160</p> </td> <td> <p>Orchards</p> </td> </tr> </tbody> </table> <p> </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. </p> <p>The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). <br> </p> <p>The maps are provided as GeoTiff files together with QGIS legend files for visualization. </p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
IHH - Contributions of Small-scale Fisheries
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Data supporting the findings of "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale"
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Text data for paper "Sharing emotions at scale: The Vent dataset"
<p>This text dataset, available upon request, is linked to the paper "<strong>Sharing emotions at scale: The Vent dataset </strong>(<a href="https://arxiv.org/abs/1901.04856">arXiv:1901.04856</a> [cs.SI]).</p> <p>Researchers who want to access the texts for research and non-commercial use are welcome to contact us.</p>
Data and Code: Beetles and fire landscape-scale BC
<p>No description provided.</p>
Cultural influences on word meanings revealed through large-scale semantic alignment: cultural measures
<p>Data files for Thompson, Roberts & Lupyan (in prep): Cultural influences on word meanings revealed through large-scale semantic alignment. This repository includes data and scripts to create the cultural similarity measure and analyse the relationships between cultural similarity and semantic alignment. This is a release of the GitHub repository here: https://github.com/seannyD/ImputeEACulturalDifferences</p>
The derived catchment-scale hydrometeorological data for hydrological Ep
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Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest - Raw SPPA data
<p>Raw data allowing the reproduction of the spatial point pattern analysis detailed in Beyns, R. et al. (2021) Fine-scale tree spatial patterns are shaped by dispersal limitation which correlates to functional traits in a natural temperate forest. <em>Journal of Vegetation Science</em>.</p>
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)
<p>Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches. </p> <p>We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model. </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube. </p> <p>For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022). </p> <p>The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map). </p> <p>The maps are provided as GeoTiff files together with a QGIS legend file for visualization. </p> <p>Class catalogue:</p> <p>10 Grassland<br> 31 Winter wheat<br> 32 Winter rye<br> 33 Winter barley<br> 34 Other winter cereal<br> 41 Spring barley<br> 42 Spring oat<br> 43 Other spring cereal<br> 50 Winter rapeseed<br> 60 Legume<br> 70 Sunflower<br> 80 Sugar beet<br> 91 Maize<br> 92 Maize (grain)<br> 100 Potato<br> 110 Grapevine<br> 120 Strawberry<br> 130 Asparagus<br> 140 Onion<br> 150 Hops<br> 160 Orchard<br> 181 Carrot<br> 182 Other vegetables<br> 555 Small woody features<br> 999 Other agricultural areas</p> <p> </p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
Large-scale vibroacoustic simulations using parallel direct solvers for high-performance clusters - Dataset
<p>The dataset contains files to retreive the matrices used as benchmark for the contribution - Sreekumar, H. K. et. al. "Large-scale vibroacoustic simulations using parallel direct solvers for high-performance clusters", DAGA Conference 2021, Vienna, 2021.</p>
Scaled and Translated Image Recognition (STIR) Source Data
<p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Original Source Data</strong></p> <ul> <li><code>dota/</code> (from <a href="https://captain-whu.github.io/DOTA/dataset.html">DOTA v1.5 Google Drive</a> website) <ul> <li><code>train/</code> <ul> <li><code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> <li><code>part2.zip</code> <strong>not</strong> unzipped</li> <li><code>part3.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>val/</code> <ul> <li><code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> </ul> </li> </ul> </li> <li><code>fontawesome/</code> (from <a href="https://fontawesome.com/v5/download">Font Awesome</a> 5.15.3 "Free for Desktop") <ul> <li><code>svgs/</code> unzipped from archive</li> </ul> </li> <li><code>mapillary/</code> (from <a href="https://www.mapillary.com/dataset/trafficsign">Mapillary Traffic Sign Dataset</a>) <ul> <li><code>mtsd_v2_fully_annotated</code> unzipped from archive</li> <li><code>train.0.zip</code> <strong>not</strong> unzipped</li> <li><code>train.1.zip</code> <strong>not</strong> unzipped</li> <li><code>train.2.zip</code> <strong>not</strong> unzipped</li> <li><code>val.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>mnist/</code> (from <a href="http://yann.lecun.com/exdb/mnist/">Yann LeCun</a> website) <ul> <li><code>t10k-images-idx3-ubyte.gz</code></li> <li><code>t10k-labels-idx1-ubyte.gz</code></li> <li><code>train-images-idx3-ubyte.gz</code></li> <li><code>train-labels-idx1-ubyte.gz</code></li> </ul> </li> </ul> <p><strong>License and Attribution</strong></p> <p>When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study.
<p>Magon A, Conte G, Arrigoni C, Dellafiore F, de Maria M, Pittella F, Rocco G, Stievano A, Ghizzardi G, Caruso R. Development and Psychometric Validation of the Nursing Self-Efficacy Scale for Managing Cancer Treatment-Induced Cardiotoxicity: An Exploratory Mixed-Method Study. Semin Oncol Nurs. 2022 Nov 18:151367. doi: 10.1016/j.soncn.2022.151367. Epub ahead of print. PMID: 36411124.</p> <p>Abstract</p> <p><strong>Objective: </strong>Assessing nursing self-efficacy could be strategic to sustain nursing competence. This study aimed to develop and validate the nursing self-efficacy scale for managing cancer treatment-induced cardiotoxicity (NSS-CTC).</p> <p><strong>Data sources: </strong>An exploratory mixed-method study was performed by including two main phases. The first comprised the developmental tasks to generate the initial pool of items, including a literature review and a consensus meeting based on a nominal group technique. The second phase initially involved an external panel of experts in assessing the content validity of the novel scale, followed by a cross-sectional data collection to perform exploratory factor analysis by employing a multicenter and convenience sampling approach. The most plausible psychometric structure derived from the exploratory factor analysis was tested with a confirmatory factor analysis using a second data collection round on another sample enrolled with a multicenter and convenience sampling approach. Internal consistency was assessed using Cronbach's alfa.</p> <p><strong>Conclusion: </strong>The NSS-CTS is a novel 15-item self-report measure for assessing nurse self-efficacy in dealing with cancer treatment-related cardiotoxicity. Its two plausible domains were labeled knowledge-related self-efficacy (Cronbach's α = 0.924) and practice-related self-efficacy (Cronbach's α = 0.937); the factor analyses in both samples showed adequate fit to sample statistics. Future studies are necessary to corroborate its construct validity and assess its measurement invariance across various country contexts.</p> <p><strong>Implications for nursing practice: </strong>Assessing nursing self-efficacy for managing cancer treatment-induced cardiotoxicity is a promising approach for identifying educational gaps and promoting nursing competency in this particular area of cancer care.</p>
Development and validation of the parents' healthcare needs scale for adolescents with congenital heart disease.
<p>Dellafiore F, Arrigoni C, Flocco SF, Barello S, Pagliara F, Bascape B, Nania T, Baroni I, Russo S, Vangone I, Conte G, Magon A, Chessa M, Caruso R. Development and validation of the parents' healthcare needs scale for adolescents with congenital heart disease. J Pediatr Nurs. 2022 Dec 16:S0882-5963(22)00321-9. doi: 10.1016/j.pedn.2022.12.006. Epub ahead of print. PMID: 36529596.</p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>The healthcare needs of parents of adolescents with congenital heart disease (CHD) have been under-investigated as no valid and reliable tools have been developed for assessing their needs. Therefore, this study aims to develop and validate the Parents' Healthcare Needs Scale for adolescents with CHD (PHNS-CHD).</p> <p><strong>Design and methods: </strong>A multi-method approach and multi-phase design were employed. Phase one referred to generating scale items based on emerging themes in the literature, and phase two showed the validation process, divided into three steps. Step one tested the content and face validity of the first version of the PHNS-CHD. After that, step two described the initial psychometric validation process of scale using an exploratory factorial analysis (EFA). Then, step three confirmed the PHNS-CHD factorial structure and assessed its internal consistency.</p> <p><strong>Results: </strong>The PHNS-CHD showed evidence of face and content validity, adequate construct, and internal consistency and stability. Specifically, it had 22 items grouped into five domains, labeled as follows: Healthcare education to the child; to be supported as a parent, clinical support to the child, the continuum of care to the child; emotional support to the child.</p> <p><strong>Conclusions: </strong>The PHNS-CHD is a psychometrically robust measure for assessing the healthcare needs of parents of adolescents with CHD.</p>
dataset relate to article "Adults with spinal muscular atrophy a large-scale natural history study shows gender effect on disease"
<p>Dataset contains clinical anonymized data of patients involved in study mentioned at title</p>
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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.