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60 results for “material extraction”
Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials
<p>Data extraction table for scoping review on best practices for co-creating patient-facing educational materials</p>
Data for Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates
<p>This repository contains data and scripts to reproduce results that are presented in the manuscript: Van Laere, J., Merckx, R., Hood-Nowotny, R., Dercon, G. (2023) Water deficit and potassium affect carbon isotope composition in cassava bulk leaf material and extracted carbohydrates. <em>Front. Plant Sci</em>. 14:1222558 doi: 10.3389/fpls.2023.1222558</p>
Supplementary Material: Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood
<p>Supplementary figures for Figure 4. Automated threshold values using the neighborhood valley method of water references in the red elliptical areas from the HH, HV and HH + HV sigma-naught values. The dashed lines represent unimodal distributions, and the solid lines represent bimodal distributions.</p>
Intermediate data files from the compilation of Economy-wide Material Flow Accounts for the Domestic Extraction of abiotic materials
<p>These files represent a selection of intermediary files from the compilation of material flow accounts on Domestic Extraction (DE) of abiotic materials. The main output of this compilation has been integrated in the UNEP IRP Global Material Flow Database (GMFD).</p> <p>These files include input files (e.g. IDs for data harmonization, or factors for data conversion), as well as output files (e.g. supplementary information, or detailed data accounts before aggregation and integration into the GMFD).</p> <p>Please note: These files are published exclusively for the purpose of making this information available to interested parties in a transparent and orderly fashion, in particular to other research projects who may have use for it. Therefore, these files are not associated with any publication and have not been adjusted or formatted with regard to any publications standards, i.e. they are uploaded exactly as they have been processed in the respective R Github repository of the underlying data compilation.</p> <p>The following description attempts to give a short overview of the respective types of files and their contents. For more detailed information on the data compilation, please refer to chapters 6, 8, and 10 in the <a href="https://resourcepanel.org/sites/default/files/irp_technical_annex_global_material_flows_database.pdf">technical report</a> of the GMFD.</p> <p> </p> <p><strong>Main data output (i.e. detailed material flow accounts)</strong></p> <p><em>DE_met_min_fos_CCC_2021-11-04.csv</em>: Data aggregated to the official categories (CCC/TCCC) used for integration into the GMFD. With IDs, without names (e.g. for materials and countries).</p> <p><em>DE_met_min_fos_CCC_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p><em>DE_met_min_fos_Detailed_2021-11-04.csv</em>: Detailed accounts, as compiled, before final aggregation. With IDs, without names.</p> <p><em>DE_met_min_fos_Detailed_with_names_2021-11-04.csv</em>: Same as above, but with names.</p> <p> </p> <p><em>DE_met_min_fos_Detailed_2022-05-24.csv: </em>Slightly revised version from May 2022. But not included in current GMFD version.</p> <p> </p> <p><strong>ID and concordance tables</strong></p> <p><em>ccc_vs_mat_ids.csv:</em> Concordance table for allocation of detailed material accounts to aggregated CCC accounts.</p> <p><em>country_ids.csv:</em> General ID table for country IDs and names</p> <p><em>estimated_ids.csv:</em> Material IDs which are assigned during application of ore estimation factors.</p> <p><em>geo_exist.csv:</em> Table for consistent geographic adjustment of data for specific countries which have disintegrated over time (not including regions like Germany, Yemen, Ethiopia, Sudan, which all have been dealt with individually if necessary).</p> <p><em>material_ids.csv:</em> General ID table for material IDs and names.</p> <p><em>source_country_ids.csv:</em> Concordance table for country IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_material_ids.csv:</em> Concordance table for material IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>source_unit_ids.csv:</em> Concordance table for unit IDs (i.e. for allocation of harmonized IDs to source namings/IDs)</p> <p><em>unit_ids.csv:</em> General ID table for unit IDs and names.</p> <p> </p> <p><strong>Conversion factors</strong></p> <p><em>conversion_elements.csv:</em> Factors applied for conversion from metal compounds to elemental metals.</p> <p><em>conversion_factors_units.csv:</em> Factors applied for unit conversions.</p> <p> </p> <p><strong>Ore estimation factors</strong></p> <ul> <li>These factors represent content-to-ore ratios (i.e. "tonnes of extracted ore/mineral per ton of produced content”).</li> </ul> <p><em>all_integrated_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (all which have been compiled/integrated)</p> <p><em>applied_est_fac_2021-11-04.csv:</em> Factors for estimation of metal and mineral ores (which have actually been applied)</p> <p><em>average_metal_prices_1990-2020.csv:</em> Metal prices applied in the compilation of estimation ratios.</p> <p><em>raw_metal_to_ore_ratios_fineprint.csv:</em> Raw metal-to-ore ratios derived from FINEPRINT mining data.</p> <p><em>raw_metal_to_ore_ratios_snl.csv:</em> Raw metal-to-ore ratios derived from SNL mining data.</p> <p> </p> <p><strong>Intermediate files from data processing</strong></p> <p><em>all_interm_conv_integr_2021-12-13.csv:</em> Harmonized, converted (units & elemental metals), integrated (i.e. without double counting). Before any estimations (ores & construction minerals) and before final cleaning/formatting.</p> <p><em>wmd_bgs_usgs_interm_harmonized_2021-12-13.csv:</em> All harmonized raw data from WMD/BGS/USGS, before any further processing (i.e. with double counting).</p> <p> </p> <p><strong>Comparison data (for mining accounts from FINEPRINT project)</strong></p> <p><em>data_for_comparison_2022-05-24.csv:</em> Data set specifically compiled for comparison with accounts on production of mines from the FINEPRINT project. Has been applied for verification of data published in <em>"Jasansky et al. (2022) An open database on global coal and metal mining"</em>.</p> <ul> <li>Includes all available types of materials which have been reported (e.g. ores and metals and metal compounds) <ul> <li>meaning: also materials which are associated with each other, for example iron ore and iron (and would therefore have been selectively integrated/excluded in the GMFD compilation).</li> </ul> </li> <li>Excludes any double counting for the exact same material from different data sources</li> <li>Harmonized IDs, converted units</li> <li>Includes metal compounds approximated/converted from reported elemental metals (where possible)</li> </ul> <p> </p> <p><strong>materialflows.net</strong></p> <p><em>data_sunburst_material_profiles_20220607.csv:</em> Full detail of data underlying the Sunburst visualization "Global Domestic Extraction in 2019, by material group" in section "Raw Material Profiles" on materialsflow.net. However, for all available years. Includes data on Domestic Extraction of biomass. Includes detail for CCC, MFA13+, MFA4+</p> <p> </p> <p><strong>Outliers</strong></p> <p><em>overview_adjusted_outliers_2021-12-22.csv:</em> Overview of outliers which were adjusted during final formatting.</p> <p> </p> <p><strong>Other</strong></p> <p><em>approximation_tailings_detailed_2021-09-26.csv:</em> Estimation of tailings based on reported amounts of ores/minerals and their respective contents.</p> <p> </p>
AiNU data for Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
<p>This file contains the AiNU data used for the article entitled by <em>Physics-based material parameters extraction from perovskite experiments via Bayesian optimization</em> (https://arxiv.org/abs/2402.11101).</p>
Fig. 3. Agarose gel image Fig. 4 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 3. Agarose gel image Fig. 4. Agarose gel image (successful PCR amplification) (failed PCR amplification) M: marker (bp) M: marker (bp) A1: Agonum fuliginosum Panzer, 1809 A: Agonum fuliginosum Panzer, 1809 A2: Agonum thoreyi Dejean, 1828 O: Omophron aequale aequale Morawitz, 1863 O: Omophron aequale aequale Morawitz, 1863 N: Notiophilus semistriatus Say, 1823 N: Notiophilus semistriatus Say, 1823 Nk: negative control. Nk: negative control.
Fig. 1 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 1. Photo of Omophron aequale jacobsoni Fig. 2. Photo of Omophron aequale jacobsoni Semenov, 1922 before incubation. Semenov, 1922 after 16 h (56°C) incubation time in tissue lysis buffer with proteinase K.
A dataset of 1600 images extracted from 5 cm RGB orthophotos for the classification of 12 classes of roofing materials
<p>This dataset contains a collection of 1601 image tiles of 64x64 pixels (3.2x3.2m²) annotated for 12 roofing materials. These tiles were extracted from 5 cm RGB orthophotos acquired by the city of Namur (Belgium) in 2017. The additional data used to create this dataset are (a) a Namur roof section mask, and (b) a set of 1601 material samples acquired using stratified random sampling. The tiles were obtained as follows: the centroid of each roof section containing a sample is used to extract tiles. A size of 64x64 pixels has been chosen so that a tile contains information for only one roof section, in order to learn only the colour and texture of the roof materials. This also avoids adding information outside the given roof section. The tiles are thus extracted for each orthophoto spectral band and labelled with the identifier of the class of roofing materials to which they belong. Here are the 12 material classes considered, preceded by their labels:</p> <p>0- Solar panels<br>1- Brown tiles<br>2- Orange tiles<br>3- Black tiles<br>4- Dark membranes<br>5- White membranes<br>6- Slates containing asbestos<br>7- Slates without asbestos<br>8- Corrugated asbestos-cement sheets<br>9- Gravel<br>10- Vegetation<br>12- Metals</p> <p>There are approximately 140 tiles by material class except for the vegetated roof sections (class 10) which contains only 47 samples due to its rarety.</p> <p>The dataset contains 1 folder for each spectral band. Each folder contains 1601 thumbnails in tif format named as follows:</p> <p><strong>img[tile id]_[class label].tif</strong></p> <p>It is suggested to apply pre-processing to these images as done by<a href="https://doi.org/10.1109/jurse57346.2023.10144142"> Wyard et al. (2023).</a></p>
Supplementary materials of Brain Extract of Subacute Traumatic Brain Injury Promotes Neuronal Differentiation of Human Neural Stem Cells Via Autophagy
<p>Figure S1: Characterization of hNSCs. In proliferate medium, the embryo derived hNSCs could form the neurospheres (A). Markers of NSCs (NES, SOX2, SOX1) were detected (B). The SOX2<sup>+</sup> cells accounted for >95% of all cells (C). Bar scale: 60μm;</p> <p>Figure S2: The proliferation of hNSCs in TBI brain extracts with different phases. Differentiated hNSCs could be labeled with EdU in TBI brain extracts with different phases (A). Ratio of EdU<sup>+</sup> cells was decreased with the progression of days post-injury (B). Bar scale: 75μm; ****: P<0.0001;</p> <p>Figure S3: The GO annotations and KEGG pathway analysis of different expressed pro-teins in acute brain extract and subacute brain extract;</p> <p>Table S1: The protein profiles of brain extract in acute phase and subacute phase after TBI.</p>
Supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale
<p>This file contains the supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale (<em>submitted</em>). </p> <p>For more details see the corresponding manuscript (once it is published) and the github repository (https://github.com/SophievF/Global_Mox_analysis/tree/main). </p>
VasTexture: Vast repository of textures and PBR Materials extracted from images using unsupervised approach
<h2><strong>VasTexture: Vast repository of textures and SVBRDF/PBR Materials extracted from images using an unsupervised approach.</strong></h2> <p> </p> <p>This dataset contains hundreds of thousands of textures and PBR/SV-BRDF materials extracted from real-world natural images.</p> <p> </p> <p>The repository is composed of RGB images of textures given as RGB images (each image is one uniform texture) and folders of PBR/SVBRDF materials given as a set of property maps (base color, roughness, metallic, etc).</p> <p>Note that this contain subset of repository more could be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>Visualisation of sampled PBRs and Textures can be seen in: <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a></p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">Link to the main project page</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Link to paper</a></p> <p> </p> <h2>File structure</h2> <p>Texture images are given in the <strong><a href="../records/11391127/files/Extracted_textures_1.zip?download=1" target="_blank" rel="noopener">Extracted_textures_</a>*.zip</strong> files.</p> <p>Each image in this zip file is a single texture, the textures were extracted and cropped from the <a href="https://storage.googleapis.com/openimages/web/index.html" target="_blank" rel="noopener">open images dataset</a>. </p> <p> </p> <p>PBR Materials are available in <strong><a href="../records/11391127/files/PBR_O0_1.zip?download=1" target="_blank" rel="noopener">PBR_*.zip</a></strong> files these PBRs were generated from the texture images in an unsupervised way (with no human intervention). Each subfolder in this file contains the properties map of the PBRs (roughness, metallic, etc, suitable for blender/unreal engine). Visualization of the rendered material appears in the file Material_View.jpg in each PBR folder.</p> <p>PBR materials and textures which are larger then 512x512 pixels contain '<strong>large' </strong>or<strong> 'larger' </strong>in their file name there about 40k in this repository but 100k more can be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>PBR materials and textures who are seamless marked as contain<strong> 'seamless'</strong> in their file name. </p> <p> </p> <p>PBR materials that were generated by mixing other PBR materials are available in files with the names<a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1"> </a><strong><a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1">PBR_mix*.zip</a> </strong></p> <p> </p> <p>Samples for each case can be found in files named: <strong> Sample_*.zip</strong></p> <p> </p> <p>File with the word <strong>Seamless </strong>contain tileable seamless textures this files also contain textures and PBR>512 size that were extracted from the Segment Anything Dataset.</p> <p>Since the textures were extracted fom natural images they were not natively seamless but were turned to seamless using the code at <strong><a href="https://github.com/sagieppel/convert-image-into-seamless-tileable-texture">this url</a></strong></p> <p> </p> <p> </p> <p><strong>Documented code used to extract the textures and generate the PBRs is available at: </strong></p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <h2>Details:</h2> <p>The materials and textures were extracted from real-world images using an unsupervised extraction method (code supplied). As such they are far more diverse and wide in scope compared to existing repositories, at the same time they are much more noisy and contain more outliers compared to existing repositories. This repository is probably more useful for things that demand large-scale and very diverse data, yet can use noisy and lower quality compared to professional repositories with manually made assets like ambientCG. It can be very useful for creating machine learning datasets, or large-scale procedural generation. It is less suitable for areas that demand precise clean and categorized PBR like CGI art and graphic design. For preview It is recommended to look at <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a> or download the Sample files and look at the Material_View.jpg files to visualize the quality of the materials.</p> <h3>Scale:</h3> <p>Currently, there are a few hundred of thousands PBR materials and textures but the goal is to make this into over a million in the near future.</p> <h2>Data generation code:</h2> <p>The Python scripts used to extract these assets are supplied at: </p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <p>The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials. </p> <h2>Alternative download sources:</h2> <p>Alternative download sources:</p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">https://sites.google.com/view/infinitexture/home</a></p> <p><a href="https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70" target="_blank" rel="noopener">https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70</a></p> <p><a href="https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz" target="_blank" rel="noopener">https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz</a></p> <p> </p> <h2>Paper</h2> <p>This work was done as part of the paper "<a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Learning Zero-Shot Material States Segmentation,</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">by Implanting Natural Image Patterns in Synthetic Data</a>".</p> <p>@article{eppel2024learning,</p> <p> title={Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data},</p> <p> author={Eppel, Sagi and Li, Jolina and Drehwald, Manuel and Aspuru-Guzik, Alan},</p> <p> journal={arXiv preprint arXiv:2403.03309},</p> <p> year={2024}</p> <p>}</p> <p> </p> <h2>License:</h2> <p>All the code and repositories are available on CC0 (free to use) licenses.</p> <p>Textures were extracted from the open images dataset which is an Apache license.</p>
Sorption strategies for recovering critical raw materials: Extracting trace elements from saltworks brines
<p><span>Rising population, industrialisation, and resource demand intensify resource limitations, particularly critical for European Union (EU) industries dependent on raw materials. Based on economic importance and supply risk, 32 Critical Raw Materials (CRMs) and two strategic raw materials were identified in 2023. With most CRMs sourced outside the EU, sea mining emerges as a promising secondary resource for CRMs extraction from seawater, although the harvest of Trace Elements (TEs) will require energy-intensive processes. Several EU-funded projects explore circular economy and resource recovery, considering sorption methods’ potential to extract TEs from brines. Commercial polymeric (IRC747, S940, MTX8010) and inorganic (SbTreat, SrTreat) sorbents, plus another synthesised inorganic one (CuHCF), were assessed for recovering TEs (cobalt, gallium, germanium, rubidium, strontium, caesium) from saltworks brines (bitterns). Polymeric sorbents, containing chelating functional groups, effectively targeted cobalt, gallium, and strontium, commercial inorganic sorbents favoured cobalt, gallium, germanium and strontium, and CuHCF targeted rubidium and caesium. Kinetic batch experiments demonstrated rapid element retention (≤30 min) by most sorbents. In dynamic column experiments, high sorption capacities were observed for cobalt and gallium with the polymeric sorbents, particularly for aminophosphonic sorbents (IRC747 and S940) (≥2.1 mg/g). SrTreat exhibited also a high sorption capacity of 7 mg/g for strontium, SbTreat achieved 20 mg/g for gallium and germanium and CuHCF retained rubidium (10 mg/g) and caesium (70 mg/g). Acidic desorption effectively recovered (>70 %) most of the elements from the sorbents, achieving concentration factors up to 708 for cobalt with IRC747 and S940, highlighting the potential valorisation of saltworks bitterns.</span></p>
Materials Datasets with 273 compositional and structural features extracted from Matminer
<p>Materials Datasets with 273 compositional and structural features extracted from <a href="https://github.com/hackingmaterials/matminer">Matminer</a>. Materials datasets are retrieved using the python package <a href="https://github.com/usnistgov/jarvis">jarvis-tools</a>.</p>
Ridge Preservation Following Tooth Extraction Using Porcine and Bovine Xenograft Materials
ClinicalTrials.gov study NCT02802319. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Extracting DNA from 'jaws': high yield and quality from archived tiger shark (Galeocerdo cuvier) skeletal material
Archived specimens are highly valuable sources of DNA for retrospective genetic/genomic analysis. However, often limited effort has been made to evaluate and optimize extraction methods, which may be crucial for downstream applications. Here, we assessed and optimized the usefulness of abundant archived skeletal material from sharks as a source of DNA for temporal genomic studies. Six different methods for DNA extraction, encompassing two different commercial kits and three different protocols, were applied to material, so-called bio-swarf, from contemporary and archived jaws and vertebrae of tiger sharks (Galeocerdo cuvier). Protocols were compared for DNA yield and quality using a qPCR approach. For jaw swarf, all methods provided relatively high DNA yield and quality, while large differences in yield between protocols were observed for vertebrae. Similar results were obtained from samples of white shark (Carcharodon carcharias). Application of the optimized methods to 38 museum and private angler trophy specimens dating back to 1912 yielded sufficient DNA for downstream genomic analysis for 68% of the samples. No clear relationships between age of samples, DNA quality and quantity were observed, likely reflecting different preparation and storage methods for the trophies. Trial sequencing of DNA capture genomic libraries using 20 000 baits revealed that a significant proportion of captured sequences were derived from tiger sharks. This study demonstrates that archived shark jaws and vertebrae are potential high-yield sources of DNA for genomic-scale analysis. It also highlights that even for similar tissue types, a careful evaluation of extraction protocols can vastly improve DNA yield.
Dataset of "Influence of the used solvent on the efficiency of the extraction of cathode materials for direct recycling"
<p>Lithium-ion batteries are playing an increasingly significant role in modern mobility and energy storage. Their increasing quantity and the limited amount of material resources available on earth will place great demands on the most efficient recycling in the future. In this paper, we focused on the first stage of the so-called direct recycling, i.e. the extraction of electrode material from the current collector, using six types of solvents in combination with three types of the most commonly used cathode materials in lithium-ion batteries. Three selected lithium-ion batteries were aged for 500 cycles and disassembled for the next step - cathode material extraction. The extraction was carried out by a method that is least energy intensive, cost effective and preserves the structural stability of the cathode materials after extraction. Of the used solvents, Dimethylacetamide (DMAC) was found to be the most effective solvent for the extraction. At the same time, Dimethylsulfoxide (DMSO) was the most effective solvent in terms of solvents considered to be biologically harmless. The study also included an analysis of the degradation of the cathode materials after their extraction from the current collector. It was found that all materials remained stable even one month after the extraction.</p>
FIGURE. Cladosporium aulonemiae (VIC 44413, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–G. Macronematous conidiophores and numerous conidia; H–I. Formation of loci in close succession; I. Spread polysaccharide-like material; J. Micronematous conidiophores; K. Ramoconidia and conidia; L. Microcyclic conidiogenesis; M. Stromatic hyphal aggregation. Scale bars: E–M = 20 µM. in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. Cladosporium aulonemiae (VIC 44413, holotype). A–D. Colonies on A. Potato dextrose agar; B. Malt extract agar; C. Oatmeal agar; D. Synthetic nutrient-poor agar, after 14 days at 25 ºC, under near-ultraviolet light, respectively. E–G. Macronematous conidiophores and numerous conidia; H–I. Formation of loci in close succession; I. Spread polysaccharide-like material; J. Micronematous conidiophores; K. Ramoconidia and conidia; L. Microcyclic conidiogenesis; M. Stromatic hyphal aggregation. Scale bars: E–M = 20 µM.
Supplementary material 1 from: Giurginca A, Šustr V, Tajovský K, Giurginca M, Matei I (2015) Spectroscopic parameters of the cuticle and ethanol extracts of the fluorescent cave isopod Mesoniscus graniger (Isopoda, Oniscidea). In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 111–125. https://doi.org/10.3897/zookeys.515.9395
Autofluorescence of living Mesoniscus graniger: Explanation note: Living Mesoniscus graniger individuals recorded by Canon EOS camera on the cave sediment inside Ardovská Cave (Slovak Karst, Slovakia) under white LED lamp and UV lamp consecutively.
Supplementary material 2 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S2. Effects of water extracts of leaves of various plants on the numbers of egg pods laid when presented to adult female desert locusts mixed with sand.
Supplementary material 1 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S1. Effects of water extracts of desert locust frass after feeding on various plants on the numbers of egg pods laid when adult female desert locusts were presented with extracts mixed with sand.
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.