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12,393 results for “Materiality”
Figure 5. Sapho venusta Karsch,1889 in The type material of Calopterygidae in the Museum für Naturkunde in Berlin (Odonata)
Figure 5. Sapho venusta Karsch,1889,junior synonym of Sapho orichalcea McLachlan, 1869; female holotype in coll. MNB (♀ 4a9572), dorsal view.
Supplementary material 1 from: Stoev P, Akkari N, Zapparoli M, Porco D, Enghoff H, Edgecombe G, Georgiev T, Penev L (2010) The centipede genus Eupolybothrus Verhoeff, 1907 (Chilopoda: Lithobiomorpha: Lithobiidae) in North Africa, a cybertaxonomic revision, with a key to all species in the genus and the first use of DNA barcoding for the group. ZooKeys 50: 29-77. https://doi.org/10.3897/zookeys.50.504
Locality dataset for all published and new records of <em>Eupolybothrus nudicornis</em> and <em>Eupolybothrus kahfi</em> in North Africa. File format: Microsoft Excel (1997-2003).
FIGURE 6 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 6. Sergio guassutinga (Rodrigues, 1971): male (cl 20.5 mm) from Icapuí, Ceará, Brazil (MZUSP 32610), A, dorsal view; B, lateral view; C, left (major) first cheliped, lateral view. Scale bars: 8 mm.
FIGURE 2. Neocallichirus maryae Karasawa, 2004 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 2. Neocallichirus maryae Karasawa, 2004: male (cl 17.8 mm) from Icapuí, Ceará, Brazil (MZUSP 34939), A, telson, dorsal view; B, same, detail of posterolateral margin; C, right uropod, dorsal view. Scale bars as indicated.
FIGURE 9 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 9. Sergio guara (Rodrigues, 1971): male (cl 10.5 mm) from Icapuí, Ceará, Brazil (MZUSP 35380), A, dorsal view; B, lateral view; C, first chelipeds, right lateral view; D, major first cheliped, left lateral view. Scale bars: 10 mm.
FIGURE 1. Neocallichirus maryae Karasawa, 2004 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 1. Neocallichirus maryae Karasawa, 2004: male (cl 8.0 mm) from Paracuru, Ceará, Brazil (LIMCE-UFC 515), A, dorsal view; B, lateral view; C, left (major) first cheliped, lateral view. Scale bars: 4 mm.
FIGURE 7 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 7. Sergio guassutinga (Rodrigues, 1971) (C, D): male (cl 20.5 mm) from Icapuí, Ceará, Brazil (MZUSP 32610), A, telson, dorsal view; B, same, detail of posterolateral margin; C, right uropod, dorsal view. Scale bars as indicated.
FIGURE 5 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 5. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, major first cheliped, lateral view, in situ; B, minor first cheliped, lateral view; C, same, detail of fingers, mesial view; D, first pleopod, ventral view; E, second pleopod, ventral view. Scale bars as indicated.
FIGURE 4 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 4. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, frontal margin and frontal appendages, dorsal view; B, same, lateral view; C, left third maxilliped, distal four articles, lateral view; D, telson, dorsal view; E, same, detail of posterolateral margin; F, right uropod, dorsal view. Scale bars as indicated.
FIGURE 3 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 3. Neocallichirus cacahuate Felder & Manning, 1995: male (cl 8.4 mm) from Icapuí, Ceará, Brazil (MZUSP 32615), A, dorsal view; B, C, lateral views. Scale bars: 6 mm.
FIGURE 8 in Re-identification of the material of Neocallichirus maryae Karasawa, 2004 from Ceará, northeastern Brazil, with the first record of N. cacahuate Felder & Manning, 1995 in the southwestern Atlantic
FIGURE 8. Neocallichirus grandimana (Gibbes, 1850): male (cl 24.2 mm) from Meireles, Ceará, Brazil (NHMW 25556), A, dorsal view; B, left lateral view; C, first chelipeds, right lateral view; D, frontal region, dorsal view. Scale bars: 20 mm [A–C], 4 mm [D].
Datasets for figure files in "A map of high mobility molecular semiconductors", Nature Materials (2017)
<p>The data contained here correspond to the figure files appearing in the work "A map of high mobility molecular semiconductors", published in Nature Materials (2017)</p>
Supplemental Materials - Performance Figures for "A Model for Predicting the (re)-occurrence of a ≥40% eGFR Decline in a large Population-based cohort of Persons with or At-Risk of Chronic Kidney Disease " paper
<p>The zip file contains performance metrics figures for each dynamic Bayesian Network (DBN) model, stratified by comorbidities, race, CKD stages, and ethnicity.</p> <p>Contains:</p> <ul> <li>Stratified: Bootstrapping of 1000 iterations and 1000 samples with stratified proportions (as in the original population of the test set) of rapid eGFR decliners and non-decliners.</li> </ul> <p> </p> <p>Second zip contains DBN structures as matrices for 2 periods study entry to entry period and entry period to year 1 for all sites in 2 excel files.</p>
Supplementary Material for The Impact of COVID-19 on Open Source Development Activities: A Multi-Method Study
<p><strong>Context:</strong> The social isolation measures resulting from the COVID-19 outbreak changed work practices in various sectors, especially with the shift to working from home. However, it is still unclear the implications of the pandemic on the maintenance and evolution of open-source software (OSS). Objective: In this study, we analyze the effects of COVID-19 on the development activity of OSS and how social isolation changed the productivity and emotional state of OSS contributors. <strong>Method:</strong><br>To investigate this issue, we have conducted a multi-method study. We first quantitatively investigated how the COVID-19 outbreak impacted the development activity of open-source projects by mining the development history of 155 open-source projects. We then qualitatively investigated the perceptions of open-source core developers about the impact of COVID-19 on their activities, using a survey as an instrument. <strong>Results:</strong> Our results indicate that the pandemic led to some effect on the development<br>activity of OSS repositories, especially in the early days of the outbreak. For instance although we observe an increase in the number of pull requests accepted, our results also indicate a sudden increase in the turnover rate of core developers. However, a few months after the declaration of the current pandemic, our results suggest stabilization in these metrics. Other effects were more far-reaching, such as a decrease in newcomers throughout the observed period after the pandemic was declared. Some findings are supported by the results of our qualitative study, whose results indicate that most of the respondents of our survey consider that COVID-19 did not change their productivity substantially. <strong>Conclusions:</strong> Our findings can help practitioners and researchers to better understand the effect of COVID-19 on the software engineering field.</p>
Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024
<table style="border-collapse: collapse; margin-left: 4.8pt; margin-right: 4.8pt; width: 99%;"> <tbody> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Science Case Name</span></strong></p> </td> <td style="padding: 5pt;"> <p> Multi-hazards in the Caribbean SIDS</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Dataset Title</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Dataset Description</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height predictions of a monocular depth estimation network trained on UAV imageries of Pichelin (2024) and Roseau (2018) trained on Google’s Open Building 2.5D Temporal dataset ( https://sites.research.google/gr/open-buildings/temporal/) and roof material classification using the model provided in GFDRR’s Caribbean Rooftop Classification (https://github.com/GFDRR/caribbean-rooftop-classification):</p> <p>· pichelin_network_height_rooftype_predictions.gpkg <a name="OLE_LINK3"></a>– Geopackage containing building height estimation and roof material classification of Pichelin, Dominica (2024).</p> <p>· roseau_network_predicitons.gpkg – Geopackage containing building height estimation and roof material classification of Roseau, Dominica (2018).</p> <p> </p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Key Methodologies</span></strong></p> </td> <td style="padding: 5pt;"> <p>A monocular depth estimation network called MiDaS (https://pytorch.org/hub/intelisl_midas_v2/) and the roof type and material classification network of GFDRR based on ResNet-50.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Temporal Domain</span></strong></p> </td> <td style="padding: 5pt;"> <p><span lang="NL">2018 and 2024</span></p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Spatial Domain</span></strong></p> </td> <td style="padding: 5pt;"> <p>Roseau and Pichelin, Dominica</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Key Indicators</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height and roof material</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Data Format</span></strong></p> </td> <td style="padding: 5pt;"> <p>A vector as a Geopackage (.gpkg)</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Source Data</span></strong></p> </td> <td style="padding: 5pt;"> <p>Openaerialmap UAV image of Roseau (2018), UAV images of Pichelin (2024) captured using a DJI drone, and Google’s Open Building 2.5D Temporal dataset</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Accessibility</span></strong></p> </td> <td style="padding: 5pt;"> <p><strong>10.5281/zenodo.17100331</strong></p> <p><strong>Disclaimer</strong><em> – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. </em></p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Stakeholder Relevance</span></strong></p> </td> <td style="padding: 5pt;"> <p>The building height and roof material dataset can be used for hazard mapping and risk management. For example, they can be integrated into the digital twin environment of buildings-at-risk.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Limitations/Assumptions</span></strong></p> </td> <td style="padding: 5pt;"> <p>Temporal alignment of images used and building height dataset the network was trained on.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Additional Information</span></strong></p> </td> <td style="padding: 5pt;">NA</td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Contact Information</span></strong></p> </td> <td style="padding: 5pt;"> <p>Bergado, John Ray (UT-ITC)</p> </td> </tr> </tbody> </table> <p> </p>
Parameters of extremal micropolar materials and procedures for FE simulation (supplementary data)
<p>These are supporting data and related procedures of the article "Extremal micropolar materials for elastic wave cloaking" to be submitted.</p>
Data for M. Kędziora, et al. "Predesigned perovskite crystal waveguides for room temperature exciton-polariton condensation and edge-lasing". Nature Materials.
<p>The repository contains data to generate figures from "Predesigned perovskite crystal waveguides for room temperature exciton-polariton condensation and edge-lasing", Nature Materials (2024).</p> <p>This work was supported by the National Science Center, Poland, under projects 2022/47/B/ST3/02411 (B.P., M. Kędziora and K.T.), 2021/43/B/ST3/00752 (M.M.) and 2019/35/N/ST3/01379 (A.O.), and financed by the European Union EIC Pathfinder Open project ‘Polariton Neuromorphic Accelerator’ (PolArt, ID: 101130304) (B.P., D.S., J.S. and M.M.). H.S. acknowledges project no. 2022/45/P/ST3/00467 co-funded by the Polish National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement no. 945339. A.O. acknowledges support from the Foundation for Polish Science (FNP). This work was supported by the joint bilateral project ‘Novel photonic platform for neuromorphic computing’ Italy MAECI–Poland NAWA PPN/BIT/2021/1/ 00124/U/00001 (K.Ł.-M., B.P., R.M., L.D.M. and D.S.). M.E., A.S. and K.B. acknowledge support from the statutory funds of the Łukasiewicz Research Network–Institute of Microelectronics and Photonics. This work had been completed while K.B. was a Doctoral Candidate in the Interdisciplinary Doctoral School at the Łódź University of Technology, Poland.</p>
Supplementary material from "Utilising citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry."
<p>Stephen H. Vickers, Jayna Raghwani, Ashley C. Banyard, Ian H. Brown, Guillaume Fournie and Sarah C. Hill </p>
Supplementary Material for Healthcare costs following falls and cataract surgery in older adults using Australian linked health data from 2012–2019
<p><strong>Note</strong>: The material contained herein is supplementary to the article:</p><p>Huang-Lung J, Chun Ho K, Lung T, Palagyi A, McCluskey P, White AJR, Boufous S, Keay L. Healthcare costs following falls and cataract surgery in older adults using Australian linked health data from 2012–2019. Public Health Res Pract. November 2023; Online early publication. https://doi.org/10.17061/phrp33342311</p><p> </p><p><strong>Contents</strong></p><p>Appendix Table A1. Details of linked administrative health data extracted for this study. </p><p>Appendix Table A2. ICD-10-AM codes for identification of fall-related hospital admissions in this study. As multiple diagnosis codes can be recorded for each hospital admission, all diagnosis codes associated with the episode of care were considered. </p><p>Appendix Table A3. SNOMED CT codes associated with fall-related emergency department presentations observed in this study. The SNOMED CT codes were searched in the CSIRO Shrimp database, version dated 31/10/21 (http://ontoserver.csiro.au/shrimp/). </p><p>Appendix Table A4. Results from the multivariable generalised linear model assessing factors associated with higher healthcare costs in study participants. </p><p>Appendix Table A5. Mean monthly healthcare costs according to cataract surgeries, sex and faller status groups, estimated from the generalised linear model using marginal means, with co-variates appearing in the model fixed at the following mean values: age = 75.5 and medications = 4.5.</p><p>Appendix Box 1. Data cleaning methods. </p><p>Figure A1. Flowchart of the study cohort. </p>
The effects of grass silage additive type and barley grain preservation method on rumen function, microbial ecology, and energy metabolism of dairy cows; supplemental material
<p>Supplementary material related to "The effects of grass silage additive type and barley grain preservation method on rumen function, microbial ecology, and energy metabolism of dairy cows"</p>
ScienceDex guides
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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.