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12,393 results for “materiality”
FIGURES 17–20 in Type material of Arctiinae (Lepidoptera: Erebidae) in the collection of the Museo Argentino de Ciencias Naturales Bernardino Rivadavia (Buenos Aires Argentina)
FIGURES 17–20. Dorsal and ventral view of type specimens with its respective labels. 17, male lectotype of Glaucopis leucostalacta Burmeister, 1880. 18, female holotype of Psoloptera meisteri Berg, 1883. 19, male holotype of Eurota (sic) julia Orfila, 1931a syn n. 20, male holotype of Eurota (sic) strigiventris famatina Orfila, 1931b.
FIGURES 5–8 in Type material of Arctiinae (Lepidoptera: Erebidae) in the collection of the Museo Argentino de Ciencias Naturales Bernardino Rivadavia (Buenos Aires Argentina)
FIGURES 5–8. Dorsal and ventral view of type specimens with its respective labels. 5, female paratype of Premolis eugenia Jörgensen, 1935. 6, female holotype of Lepidojulia arnaui Orfila, 1952. 7, male holotype of Amastus ninae Orfila, 1959. 8, female holotype of Pericopis regalis Jörgensen, 1935.
Supplementary material 4 from: Cumming RT, Bank S, Le Tirant S, Bradler S (2020) Notes on the leaf insects of the genus Phyllium of Sumatra and Java, Indonesia, including the description of two new species with purple coxae (Phasmatodea, Phylliidae). ZooKeys 913: 89-126. https://doi.org/10.3897/zookeys.913.49044
Table S4. Measurements of Phyllium gardabagusi sp. nov., holotype female, paratype males and females
Supplementary material 5 from: Cumming RT, Bank S, Le Tirant S, Bradler S (2020) Notes on the leaf insects of the genus Phyllium of Sumatra and Java, Indonesia, including the description of two new species with purple coxae (Phasmatodea, Phylliidae). ZooKeys 913: 89-126. https://doi.org/10.3897/zookeys.913.49044
File S1. Concatenated supermatrix of COI, 28S, and 16S sequences from the 21 specimens sampled (18 Phyllium and three outgroups)
Supporting material for: A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study
<p>Underlying data, R scripts and subsequent figures are presented to deliver a replicable and transparent analysis for the manuscript "A multi-batch design to deliver robust estimates of efficacy and reduce animal use – a syngeneic tumour case study"</p> <p>Published in Scientific Reports</p> <p>See: <a href="https://protect-de.mimecast.com/s/kCQLCgpRGySlzOwmCNma6d?domain=rdcu.be">https://rdcu.be/b3vj2</a> </p> <p> </p> <p>This is version 2 of this data. This differs from version 1 in the following</p> <p>1. The number of simulation has increased from 300 to 2000 in each simulation cycle</p> <p>2. Script has been added to generate publication level figures</p> <p> </p> <p> </p> <p> </p>
Supplementary material for the publication "Copper mining: 100% solar electricity by 2030?"
<p>This is a supplementary material to the publication "<a href="https://www.sciencedirect.com/science/article/pii/S0306261920300180">Copper mining: 100% solar electricity by 2030?</a>" by Jannik Haas, Simón Moreno-Leiva, Tobias Junne, Po-Jung Chen, Giovanni Pamparana, Wolfgang Nowak, Willy Kracht, and Julián Ortiz. </p> <p>This file contains the main inputs used and outputs generated. For the reasoning and assumptions behind these numbers, as well as the origin of these datasets, please consult the main article.</p>
Videos of computer vision based recognition/segmentation/classification of materials inside vessels in chemistry lab and other setting
<p>These videos contain materials in vessels in various settings related to the chemistry lab, medical samples and handling liquids in everyday life settings. The region and type of each vessel and material phase found by the computer vision (neural net) are marked in purple, the class of each phase appears above each panel in green (each panel corresponds to a different class). The work is part of the computer vision for the chemistry lab project.</p> <p>For details on the project see:</p> <p><a href="https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004">https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004</a></p> <p>For Details on the videos See:</p> <p><a href="https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ">https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ</a></p> <p>Basically, the videos contain process such as pouring, mixing, foam formation precipitation, phase separation, melting freezing, dissolving, etc.., where the region of each vessel and material phase and their type (liquid, solid, powder, suspension, foam) is found by the neural net and marked purple.</p> <p>For code see:</p> <p><a href="https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab">https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab</a></p> <p> </p> <p> </p> <p> </p>
Modeling of the potential distribution of Eichhornia crassipes on a global scale: risks and threats to water ecosystems. Supplementary material
<p>https://doi.org/10.4136/1980-993X</p>
Supplementary material 1 from: Grace JB (2020) A 'Weight of Evidence' approach to evaluating structural equation models. One Ecosystem 5: e50452. https://doi.org/10.3897/oneeco.5.e50452
This text file contains the R code used to develop the demonstrations included in Grace JB (2020) A 'weight of evidence' approach to evaluating structural equation models. One Ecosystem
Supplementary Material for "Updating and Extending an UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases"
<p>This is the supplementary material for our publication "Updating and Extending an UV/Vis Spectroscopy-Based Assay for Monitoring of Transformations Between Nucleosides and Nucleobases".</p> <p> </p> <p><strong>For a detailed description of this material, please see the "Notes on the Supporting Material".</strong></p> <p> </p> <p>Naturally, this publication builds on previous work. The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publication (10.3390/mps2030060, 10.5281/zenodo.3333469). Applications of this methodology are described in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858).</p> <p>I wish to thank all authors of this publication for their contribution, using the method and generously sharing your experience and your data with me. This helped us develop robust protocols, learn about the potential and limits of the method and ultimately made this update article possible. While I have been the primary user of the method, your applications, questions and problems have spurred us to think outside of the box and find solutions that helped us expand our repertoire of "unmixable" reactions. This Supplementary Information contains some of our shared data and I thank each of you for contributing to this work.</p> <p> </p>
Compendium of Energetic Materials Data
<p>A collection of names, elemental formulas, SMILES strings, and CAS numbers for openly published energetic molecules. More entries and types of data may be added to this compendium in future versions. We provide this data so that it may be used for research and cited (with zenodo URL and DOI) in papers.</p>
Supplementary material 2 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Use cases were collected using a number of methods to maximise responses. Lead authors of papers published using data accessed via the Atlas of Living Australia (ALA) were contacted and asked to contribute their research data use cases, and a number of papers describing fitness for use determination were sent to the ALA Data Quality group. Fitness for use and quality check information from these papers were extracted and transferred to the use case library. These are the results of those surveys.
Supplementary material 4 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Description and specifications for the tests following the conventions of the Fitness For Use Framework. This supplement is a copy of https://github.com/tdwg/bdq/blob/master/tg2/core/TG2_tests.csv as of commit 941e774 2019-Aug-20.
Supplementary material 3 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Counts of occurrence records in 2019-04-15 snapshot of GBIF-mediated data that fit the three categories of expected responses for each of the event date-related validation tests.
Supplementary material 1 from: Chapman AD, Belbin L, Zermoglio PF, Wieczorek J, Morris PJ, Nicholls M, Rees ER, Veiga AK, Thompson A, Saraiva AM, James SA, Gendreau C, Benson A, Schigel D (2020) Developing Standards for Improved Data Quality and for Selecting Fit for Use Biodiversity Data. Biodiversity Information Science and Standards 4: e50889. https://doi.org/10.3897/biss.4.50889
Vocabulary of Terms used for the TDWG Task Group on Data Quality Tests and Assertions, plus key additional terms from the Use Case Study. Ther terms are consistent with the terms used in the Fitness for Use Framework (Veiga et al. 2017)
IronRefs database of bibliographic data on remediation of contaminated environmental materials using zerovalent metals such as iron (ZVI)
<p>Database of bibliographic data for publications (journal articles, reports, patents, presentation abstracts, etc.) on the use of zerovalent metals (ZVMs)—especially zerovalent iron (ZVI)—for remediation or treatment of contaminated environmental materials (mainly groundwater, but also wastewater, soil, and sediment).</p> <p>The data were compiled and curated between 1996 and 2011 by members of the research group of Professor Paul G. Tratnyek at the Oregon Graduate Institute in Beaverton, Oregon, USA. At the beginning, the field was very small and most new work appeared first in gray-literature, so manual curation of the database made it a uniquely-valuable resource for the community.</p> <p>The original list of references was replaced with a searchable online database in 1998, which was named the “IronRefs” database and made available to all at <a href="http://cgr.ebs.ogi.edu/ironrefs/">http://cgr.ebs.ogi.edu/ironrefs/</a>. The database was updated periodically until 2011, by which time the field had grown so large that it was no longer practical to keep up with all of the new publications.</p> <p>The early development and impact of the IronRefs database was documented when it reached 500 records (Tratnyek, 2002) and when it reached 1000 records (Schneider et al., 2008). The former was published as an article in Groundwater Monitoring and Remediation (DOI: 10.1111/j.1745-6592.2002.tb00757.x) and the latter was given as a poster at the 6th International Conference on Chlorinated and Recalcitrant Compounds, Monterey, CA (PDF included here).</p> <p>In 2019, the server containing IronRefs was retired, so this unique resource became unavailable. In place of IronRefs, we provide here all of the data (2000+ records) in two tagged-field formats that are compatible with most bibliographic data management software: an EndNote compressed library (.enlx) and an RIS file (.ris). We also exported the data to a tab-delimited text file and provide that in .csv and .txt formats.</p> <p>Note that no new records were added to the IronRefs database after about mid-2011, so the value of the dataset provided here is largely historical. There are many newer publications that are relevant, and the literature on this topic continues to grow rapidly as of 2020.</p>
Supplementary Material for 'Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review'
<p>This supplementary material accompanies:</p> <p>P. H. Charlton <em>et al.</em>, “Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review,” <em>IEEE Rev. Biomed. Eng.</em>, vol. 11, pp. 2–20, 2018. DOI: <a href="https://doi.org/10.1109/RBME.2017.2763681">10.1109/RBME.2017.2763681</a></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3741462">Version 1</a>: </strong>This is the version originally accompanying the article (albeit with a few filename corrections from the published version).</p> <p><strong><a href="https://doi.org/10.5281/zenodo.3741463">Version 2</a>: </strong>This version was produced on 6th April 2020. The Excel files have been updated to .xlsx versions, and the Matlab script (BRReviewAnalyses.m) has been amended accordingly. In addition, further functionality has been added to the Matlab script (BRReviewAnalyses.m), and a new script has been added which was not previously released in the supplementary material (BRReviewSearchStrategy.m).</p>
Supplementary material 1 from: Kiran M, Sattar A, Zamir K, Haelewaters D, Nasir Khalid A (2020) Additions to the genus Chroogomphus (Boletales, Gomphidiaceae) from Pakistan. MycoKeys 66: 23-38. https://doi.org/10.3897/mycokeys.66.38659
Table S1. Taxa used in molecular phylogenetic analysis with voucher, country, and ITS GenBank accession number
Supplementary material 1 from: Bragança PHN, van Zeeventer RM, Bills R, Tweddle D, Chakona A (2020) Diversity of the southern Africa Lacustricola Myers, 1924 and redescription of Lacustricola johnstoni (Günther, 1894) and Lacustricola myaposae (Boulenger, 1908) (Cyprinodontiformes, Procatopodidae). ZooKeys 923: 91-113. https://doi.org/10.3897/zookeys.923.48420
Species localities and Genbank Acession numbers: acession numbers in bold refers to sequences developed in the present study
Long-Term Wi-Fi fingerprinting dataset and supporting material
<p>WiFi measurements database for UJI's library and supporting material.</p> <p>The measurements were collected by one person using mainly one Android smartphone during 25 months at two floor of the library building from Universitat Jaume I, in Spain. It contains 103,584 WiFi fingerprints, which are organized into datasets. Each dataset is the result of a collection campaign.</p> <p>The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples on possible studies that the database may enable. The supporting material also includes the bookshelves local coordinates.</p> <p>Citation request:</p> <p>Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. <em>Data</em> <strong>2018</strong>, <em>3</em>, 3.</p> <p>G.M. Mendoza-Silva, P. Richter, J. Torres-Sospedra, E.S. Lohan, J. Huerta, "Long-Term Wi-Fi fingerprinting dataset and supporting material", Zenodo repository, DOI 10.5281/zenodo.1066041.</p> <p> </p>
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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.