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737 results for “Silver”
Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data
<p>Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript:</p> <p>Herbst, M., Reifenberger, A., Velte, C. <em>et al.</em> Specific Heat of Holmium in Gold and Silver at Low Temperatures. <em>J Low Temp Phys</em> <strong>202, </strong>106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1</p> <p>For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.</p>
Experimental data for "Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra"
<p>Testing data (experimental data) for neural networks published in preprint https://doi.org/10.48550/arXiv.2404.10891</p> <p>The UV-VIS-NIR spectral data was also used in the dissertation of Nadzeya Khinevch, titled "Two-dimensional structures of nanoparticles for elements of surface-enhanced Raman scattering substrates".</p> <p>Emails of the corresponding authors:</p> <p>Tomas Klinavičius tomas.klinavicius@ktu.lt</p> <p>Tomas Tamulevičius tomas.tamulevicius@ktu.lt</p>
Selected data(s) from : Five-dimensional optical data storage based on ellipse orientation and fluorescence intensity in a silver-sensitized commercial glass
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p>- <strong>Figure 1.</strong> (<strong>a</strong>) Femtosecond laser tight focusing in the silver-containing glass, leading to the production of fluorescent silver clusters at its periphery. (<strong>b</strong>) SLM holographic phase masks with an additional cylindrical profile leading to an elliptical pattern by DLW. (<strong>c</strong>) Oriented elliptical patterns obtained by SLM phase mask manipulation, corresponding to 2<sup>4</sup> = 16 orientation-encoded levels. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> Fabricated fluorescence calibration matrix. (<strong>a</strong>) Confocal image of all basic storage units composed by 16 intensity levels and 16 orientation levels. (<strong>b</strong>) Measured fluorescence intensity versus incident DLW intensity for the 5D decoding process. <strong>(Pictures, opj file, csv datas)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>,<strong>b</strong>) are the encoded images of two Nobel laureates in 16 orientation levels and 16 intensity levels, respectively. (<strong>c</strong>) 100 × 100 entangled patterns among 16 × 16 intensity and orientation levels. (<strong>d</strong>) The fluorescence calibration matrix was fabricated for decoding (fluorescence excitation at 405 nm). <strong>(Pictures, cvs datas)</strong></p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_2020-10-21_V01 : Figure 3</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3a_2020-10-21_V01 : Original image oritentation</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3b_2020-10-21_V01 : Original image intensity</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3c_Figure3d_2020-10-21_V01 : DLW image</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_IICT4BF_2020-10-21_V01 : Intensity image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BF_T2020-10-21_V01 : Orientation image converted to 4 bit format</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BFL_2020-10-21_V01 : Orientation image converted to 4 bit format level</li> </ol> <p><strong>- Figure 4.</strong> (<strong>a</strong>,<strong>b</strong>) Retrieved images from the initial images of Figure 3a,b, respectively. (<strong>c</strong>,<strong>d</strong>) Histograms of the level difference between original and decoded levels for the orientation direction and the fluorescence intensity, respectively. (<strong>Picture and csv datas</strong>)</p> <p>- <strong>Figure 5.</strong> (<strong>a</strong>) Confocal top-view image of one single elliptically-shaped storage unit fabricated by using type A DLW. (<strong>b</strong>) Fluorescence intensity profile along the horizontal and vertical cross section at focal plane. (<strong>c</strong>) Fluorescence intensity profile and Gaussian fitting along the z-axis (depth). (<strong>Picture, opj file, csv datas</strong>)</p> <p> </p> <p> </p>
Selected data(s) from : Femtosecond direct laser writing of silver clusters in phosphate glasses for x-ray spatially-resolved dosimetry
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1.</strong> Microscopy fluorescence image of ARGOi glass sample (excitation at 365 nm) of laser-inscribed structures for the different writing irradiances at two different depths: (<strong>a</strong>) structures at 150 µm below the glass front surface, (<strong>b</strong>) structures at 550 µm below the glass front surface, and at 150 µm from the glass rear surface. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> (<strong>a</strong>) Transparent color before irradiation (ARGO glass sample), (<strong>b</strong>) yellow color after X-ray irradiation with 222 Gy (ARGO* glass sample). <strong>(Only picture)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>) Absorption spectra of the ARGO and ARGO* glass sample after various X-ray doses and the difference absorption coefficient spectrum for 222 Gy vs. pristine. (<strong>b</strong>) Fit of the radiation-induced spectrum (difference between 222 Gy and pristine) considering Gaussian energy contributions for ARGO and ARGO*. (<strong>c</strong>) Absorption spectra for the GPN and GPN* glasses for X-ray doses from 5 mGy to 3 kGy [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>d</strong>) The difference absorption coefficient spectra between different doses conditions for GPN and GPN* [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_2022-03-03_V01. <strong>Figure 3</strong></li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_Datas_2022-03-03_V01. Datas : <strong>wavelength, effective absorption coefficient (cm-1)</strong></li> </ol> <p>- <strong>Figure 4.</strong> Micro-luminescence of GPN* glass performed on the optically polished glass side: (<strong>a</strong>) integrated fluorescence intensity at different depths, (<strong>b</strong>) normalized spectrum evolution with depth for the 500 Gy dose [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_2022-03-03_V01. Figure 4</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_Datas_2022-03-03_V01. Datas</li> </ol> <p>- <strong>Figure 5.</strong> Estimated depth-dependent profiles in absolute values of the linear absorption coefficient at 405 nm. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_2022-03-03_V01. Figure 5</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_Datas_2022-03-03_V01. Datas : <strong>sample depth (mm) ; scaled linear absorption coefficient profile at 405 nm (mm-1)</strong></li> </ol> <p>- <strong>Figure 6.</strong> (<strong>a</strong>) X-ray energy spectra simulated by SpekPy for each irradiation facility, normalized by integral. (<strong>b</strong>) Geant4-simulated dose inside each sample, normalized by the surface dose; filled areas show uncertainties at 95% confidence. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_2022-03-03_V01. Figure 6</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_Datas_2022-03-03_V01. Datas : <strong>ARGO 100KV_dose ; GPN-20KV_dose ; GPN-32KV_dose</strong></li> </ol> <p>- <strong>Figure 7.</strong> Radio-photoluminescence measurement of the GPNi* glass for the inscribed structure [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_2022-03-03_V01. Figure 7</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_Datas_2022-03-03_V01. Datas : <strong>wavelength ; relative intensity a.u.</strong></li> </ol> <p>- <strong>Figure 8.</strong> Normalized RPL spectra excited at 325 nm: (<strong>a</strong>) for the ARGO (pristine—right axis) and ARGO* (X-ray irradiation at 222 Gy—left axis) glasses collected around 150 µm below the surface, (<strong>b</strong>,<strong>c</strong>) for the highest DLW irradiance structure for ARGOi and ARGOi* in the front- and the rear-inscribed surfaces, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_2022-03-03_V01. Figure 8</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_Datas_2022-03-03_V01. Datas : <strong>inscribed glass...</strong></li> </ol> <p>- <strong>Figure 9.</strong> (<strong>a</strong>) Differential linear absorption coefficient of the laser-inscribed structures (11 TW/cm<sup>2</sup>) for the two planes after irradiation at 222 Gy X-ray dose in the ARGOi* glass sample. (<strong>b</strong>) Average differential absorption of the inscribed structures for all DLW irradiance (as from <a href="https://www.mdpi.com/2227-9040/10/3/110/htm#fig_body_display_chemosensors-10-00110-f009">Figure 9</a>a). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_2022-03-03_V01. Figure 9</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_Datas_2022-03-03_V01. Datas : <strong>integrated differential linear absoprtion percentage ; irradiance (TW/cm2)</strong></li> </ol> <p>- <strong>Figure 10.</strong> (<strong>a</strong>) Phase image under white light illumination of the laser inscribed structure (11 TW/cm<sup>2</sup>) before irradiation. (<strong>b</strong>) Optical path difference determined from the phase image. (<strong>c</strong>) The refractive index modification Δ<em>n</em> as a function of laser irradiance before/after 222 Gy-dose for the two planes in ARGOi, ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_2022-03-03_V01. Figure 10</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_Datas_2022-03-03_V01. Datas : <strong>refractive index modification ; irradiance (TW/cm2), Error bar</strong></li> </ol> <p><strong>- Figure 11.</strong> Comparison between calculated and measured Δ<em>n</em>ˆ after irradiation for a decrease in the initial value of <em>N</em><em>α</em>3 by 0.48%: (<strong>a</strong>,<strong>c</strong>) the real part Δ<em>n</em> for the front and rear surfaces, respectively; (<strong>b</strong>,<strong>d</strong>) their imaginary counterparts Δ<em>κ</em>, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_2022-03-03_V01. Figure 11</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_Datas_2022-03-03_V01. Datas : <strong>rear surface...</strong></li> </ol> <p><strong>- Figure 12.</strong> Integrated measure of the amplitude of fluorescence intensity for the different laser irradiance before and after 222 Gy-dose for the two planes in ARGOi and ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_2022-03-03_V01. Figure 12</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_Datas_2022-03-03_V01. Datas : <strong>integrated measure of the amplitude of fluorescence intensity ; Irradiance (TW/cm2) ; Error bar </strong></li> </ol> <p><strong>- Figure 13.</strong> (<strong>a</strong>) Composite FLIM and fluorescence intensity microscopy images of the laser-induced structure (11 TW/cm<sup>2</sup>) before and after irradiation for an emission at 425 nm from the front surface; the color-code represents the mean lifetime obtained by FAST-FLIM algorithm (color scale from 0 to 31 ns); inset: luminescence intensity only (grey-scale from 0 to 45 counts). (<strong>b</strong>) Same composite FLIM and luminescence intensity images for an emission at 510 nm. (<strong>c</strong>) Luminescence decays in arbitrary units for the emission at 425 nm of the same structure before and after irradiation for the two surfaces, and fitting curves thereof using three exponential decay functions. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_2022-03-03_V01. Figure 13</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_Datas_2022-03-03_V01. Datas : <strong>fluorescence intensity (arbitrary units) ; time (ms)</strong></li> </ol> <p>- <strong>Figure 14.</strong> Dose-dependent evolution of the amplitude ratio of extracted spectral bands for (<strong>a</strong>) the GPNi* glass sample for DLW irradiance of 13.4 TW/cm<sup>2</sup> at 160 µm below the glass surface, (<strong>b</strong>) the ARGOi and ARGOi* glass sample for DLW irradiance of 11 TW/cm<sup>2</sup> at 550 µm below the glass surface (rear surface). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_2022-03-03_V01. Figure 14</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_Datas_2022-03-03_V01. Datas : <strong>ratio of amplitudes of spectral bands ; doses (gy)</strong>.</li> </ol>
The Immunomodulatory Effect of Silver Nanoparticles in a Retinal Inflammatory Environment
<p>Activation of immune response plays an important role in the development of retinal diseases. One of the main populations of immune cells contributing to the retinal homeostasis are microglia, which represent a population of residential macrophages. However, under pathological conditions, microglia become activated and rather support a harmful inflammatory reaction and retinal angiogenesis. Therefore, targeting these cells could provide protection against retinal neuroinflammation and neovascularization. In the recent study, we analyzed effects of silver nanoparticles (AgNPs) on microglia in vitro and in vivo. We showed that the AgNPs interact in vitro with stimulated mouse CD45/CD11b positive cells (microglia/macrophages), decrease their secretion of nitric oxide and vascular endothelial growth factor, and regulate the expression of genes for Iba-1 and interleukin-1β (IL-1β). In our in vivo experimental mouse model, the intravitreal application of a mixture of proinflammatory cytokines tumor necrosis factor-α, IL-1β and interferon-γ induced local inflammation and increased local expression of genes for inducible nitric oxide synthase, IL-α, IL-1β and galectin-3 in the retina. This stimulation of local inflammatory reaction was significantly inhibited by intravitreal administration of AgNPs. The application of AgNPs also decreased the presence of CD11b/Galectin-3 positive cells in neuroinflammatory retina, but did not influence viability of cells and expression of gene for rhodopsin in the retinal tissue. These data indicate that AgNPs regulate reactivity of activated microglia in the diseased retina and thus could provide a beneficial effect for the treatment of several retinal diseases.</p>
Dataset related to the publication "Lasing in an Assembled Array of Silver Nanocubes"
<p>This archive contains the raw data used to draw the graphs published in the paper "Lasing in an Assembled Array of Silver Nanocubes". </p>
Sri Ksetra, Bago, Myanmar. Reliquary in repoussé silver.
<p>Sri Ksetra, Bago, Myanmar. Reliquary in repoussé silver. Reconstruction (black and white version).</p>
Silver film response to sulfate reduction activity in alpine and subalpine wetlands, 2022.
Alpine ecosystems serve as crucial water resources for many areas of the world, and biogeochemical cycling in these regions can influence the chemistry of water flowing into downslope watersheds. Alpine and subalpine wetlands are understudied systems of particular interest since lowland wetlands are known to have high rates of biogeochemical activity that can disproportionally affect carbon (C) and nutrient uptake, sequestration, and transformations within the landscape. Wetland processes play a central role in sulfur (S) transformations and have conditions that can support sulfate reduction. Sulfate reduction determines the sequestration of S in wetlands and interacts closely with a multitude of other element cycles, including iron, carbon, nitrogen, and mercury. Previous work in Niwot alpine and subalpine wetlands noted large variability in sulfate reduction rates within wetland soils. Samples taken less than a meter away from each other sometimes showed almost 70x higher or lower rates (Rea, unpublished work). This work sought to adapt a silver film method to quantify sulfate reduction rates over small-scale spatial areas. The method proved valuable as a quick indicator of sulfate reduction activity and was able to visualize soil heterogeneity. However, the silver films were not sensitive enough to quantify sulfate reduction rates in situ.
Annotated checklist of the beetles of Abeti Soprani, a silver fir forest of Central Italy
<p>The checklist contains 179 species of beetles which belong to 48 families. The species were collected during a field study carried out in the years 2012 and 2013 and aimed at describing the beetles of the area. The collection methods consisted in window flight traps and emergence traps. The study area, named Abeti Soprani, is a silver fir (<em>Abies alba</em>) forest located in the Central Apennines.</p> <p>The checklist is annotated with information on the taxonomy of the species (order and family), number of individuals, geographic position, habitat type (following EUNIS habitat classification 2017), sampling protocol, collector name, specialist name, IUCN Red List categories of the saproxylic species (Carpaneto et al. 2015). </p> <p>The terms used for the dataset fields follows the Darwin Core Maintenance Group. 2020. List of Darwin Core terms. Biodiversity Information Standards (TDWG). <a href="https://dwc.tdwg.org/list/">https://dwc.tdwg.org/list/</a></p> <p>Investigations on spatial patterns and diversity have been based on this dataset and published (Parisi et al. 2016, 2020).</p> <p>The harmonization of the dataset to the point of view of taxa, authorship, LSID and the massive upgrading of the related identifiers in Zenodo record was performed by the use of R script using respectively dplyr, taxize (Chamberlain and Szöcs, 2013) and zen4r (Blondel and Barde, 2020) packages.</p>
Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar. Silver bowl, detail, circa seventh century.
<p>Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar (ancient Śrī Kṣetra သရေခေတ္တရာ ပြည်). Silver bowl, imported from China, circa seventh century, with Pyu inscription on the reverse.</p>
Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar. Silver bowl, circa seventh century.
<p>Tharaykhittaya Archaeological Museum, Tharakhittaya, Pyay, Myanmar (ancient Śrī Kṣetra သရေခေတ္တရာ ပြည်). Silver bowl, imported from China, circa seventh century, with Pyu inscription on the reverse.</p>
Sri Ksetra, Bago, Myanmar. Guardian figure in repoussé silver.
<p>Sri Ksetra, Bago, Myanmar. Guardian figure holding a club, in silver repoussé. Now in the National Museum of Myanmar, Yangon, as documented in 2017. Probably 6th century.</p>
ZibaldonED: Silver annotations for Entity Disambiguation from Digitalzibaldone
<p>The <em>Zibaldoned</em> dataset provides silver annotations for entity disambiguation, extracted from <em>DigitalZibaldone</em>, the HTML digital edition of Giacomo Leopardi’s <em>Zibaldone di pensieri</em>, curated by Silvia Stoyanova and Ben Johnston [1].</p> <p>The dataset was collected through a simple web scraping algorithm and includes 2,941 references to people, places, and bibliographical sources across 957 paragraphs. These annotations offer a valuable resource for tasks such as Named Entity Recognition (NER) and Entity Linking (EL) in the context of literary and linguistic research. The dataset can assist in training AI models to automatically recognize and disambiguate entities in literary works.</p> <p>The GitHub repository containing the code used to extract the dataset and train NER and EL models is available here: <a href="https://github.com/sntcristian/zibaldoned" target="_blank" rel="noopener">GitHub - zibaldoned</a>.</p> <p><br>[1] Silvia Stoyanova and Ben Johnston (Eds.), <em>Giacomo Leopardi's Zibaldone di pensieri: a digital research platform</em>.</p>
Aqueous geochemical measurements and speciation calculations with concurrent copper resistance gene counts from sediment metagenomes over a seasonal cycle from 2015 to 2016 on Silver Bow Creek and Blacktail Creek near Butte, MT
<p>This dataset contains information from concurrently gathered geochemical and metagenomic samples collected from Silver Bow Creek and Blacktail Creek near Butte, MT (SBC/BC) during 2015 and 2016. SBC/BC is recovering from metal contamination related to extensive mining in the area. Full geochemical measurements, geochemical speciation calculations, and gene counts of sequences mapping to copper resistance genes using MG-RAST are included. </p>
Potential and realized distribution at 30m for Silver fir (Abies alba) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the silver fir (<em>Abies alba, Mill.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_abies.alba_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_abies.alba_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_abies.alba_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_abies.alba_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_abies.alba_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Sri Ksetra, Bago, Myanmar. Reliquary in repoussé silver.
<p>Sri Ksetra, Bago, Myanmar. Reliquary with Pāli and Pyu inscriptions, in repoussé silver, as documented and published 1926-27 after being found in the Khin Ba relic chamber (located approximately at at 18°47'37"N 95°18'0"E).</p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
Exploration of historical mining site - Akersberg silver mines (St. Hanshaugen, Oslo, Norway, 04/11/2024)
<p>Exploration of the historical mining site of Akesrberg (St. Hanshaugen, Oslo, Norway, 04/11/2024)</p> <p>- Main ore minerals: pyrite, sphalerite, galena, chalcopyrite, argentite, stembergite</p> <p>- Provisional References:</p> <ul> <li>https://www.researchgate.net/publication/330971315_Akersberg_gruver_Akersberg_Silver_Mines_pages_168-174_in_Arnesen_R_Gjemte_og_glemte_steder_-_urban_utforsking_i_Oslo_og_omradet_rundt_In_Norwegian</li> <li>https://foreninger.uio.no/ngf/ngt/pdfs/NGT_71_2_121-128.pdf</li> <li>https://www.mindat.org/loc-37117.html</li> <li>https://no.wikipedia.org/wiki/Akersberg_gruver</li> </ul>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Sri Ksetra, Bago, Myanmar. Reliquary in repoussé silver.
<p>Sri Ksetra, Bago, Myanmar. Reliquary in repoussé silver. Reconstruction (colour version).</p>
ScienceDex guides
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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.