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919 results for “material data”
Urban material ground truth data for the 2007 HyMap hyperspectral image of Munich
<p><span>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 12028 labeled spectra derived from the 4m resolution airborne hyperspectral HyMap image of Munich (Germany) that was acquired during the summer of 2007 (June 17 and 25 2007). The labeled image spectra are retrieved from pixels of the HyMap dataset that has been processed to level 2A surface reflectance in 119 bands ranging between the wavelengths of 455 nm and 2496 nm. The preprocessing performed on this image data is explained in Heldens et al. (2008) and Heiden et al. (2012). See the "Related works" section of this data publication.</span></p> <p><span>The ground truth (GT) data have been used in previous research (again, see the "Related works" section) and they were likewise used for the remote sensing-based mapping experiments with a generic urban spectral library performed in the frame of the GENLIB research project. The data set contains reflectance spectra of typical urban surface materials and their spectral variations.</span></p> <p><span>The spectra included in this dataset were sampled from the above mentioned HyMap image by (1) using the methodology described in Jilge et al. (2017) and (2) through the delineation of manually digitized regions of interest. The image spectra are <span> </span>labeled based on the method mentioned above and using ancillary reference data, already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to the image spectra. These labels cover:</span></p> <ul> <li><span>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the </span><span><a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener"><span>website of the EAGLE framework</span></a></span><span> for more information.</span></li> <li><span>Material Groups (MG).</span></li> <li><span>Artificial Material Types (AMT).</span></li> </ul> <p><span>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</span></p> <p><span>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</span></p>
Alpha-2 Adrenoreceptor Antagonist Yohimbine Potentiates Consolidation of Conditioned Fear (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Panitz, C., Skoluda, N., Nater, U. M., Pizzagalli, D. A., Hermann, C., & Mueller, E. M. (2022). Alpha-2 adrenoreceptor antagonist yohimbine potentiates consolidation of conditioned fear. <em>International Journal of Neuropsychopharmacology</em>, 25(9), 759–773.</strong></p> <p><em>Background:</em> Hyperconsolidation of aversive associations and poor extinction learning have been hypothesized to be crucial in the acquisition of pathological fear. Previous animal and human research points to the potential role of the catecholaminergic system, particularly noradrenaline and dopamine, in acquiring emotional memories. Here, we investigated in a between-participants design with 3 groups whether the noradrenergic alpha-2 adrenoreceptor antagonist yohimbine and the dopaminergic D2-receptor antagonist sulpiride modulate long-term fear conditioning and extinction in humans.<br><em>Methods:</em> Fifty-five healthy male students were recruited. The final sample consisted of n = 51 participants who were explicitly aware of the contingencies between conditioned stimuli (CS) and unconditioned stimuli after fear acquisition. The participants were then randomly assigned to 1 of the 3 groups and received either yohimbine (10 mg, n = 17), sulpiride (200 mg, n = 16), or placebo (n = 18) between fear acquisition and extinction. Recall of conditioned (non-extinguished CS+ vs CS−) and extinguished fear (extinguished CS+ vs CS−) was assessed 1 day later, and a 64-channel electroencephalogram was recorded.<br><em>Results:</em> The yohimbine group showed increased salivary alpha-amylase activity, confirming a successful manipulation of central noradrenergic release. Elevated fear-conditioned bradycardia and larger differential amplitudes of the N170 and late positive potential components in the event-related brain potential indicated that yohimbine treatment (compared with a placebo and sulpiride) enhanced fear recall during day 2.<br><em>Conclusions:</em> These results suggest that yohimbine potentiates cardiac and central electrophysiological signatures of fear memory consolidation. They thereby elucidate the key role of noradrenaline in strengthening the consolidation of conditioned fear associations, which may be a key mechanism in the etiology of fear-related disorders.</p>
MCR LTER: Coral Reef: Material legacy disturbance type model; data for Kopecky et al., 2023 Ecology
This data package contains the code necessary to create a mathematical model of coral reef recovery dynamics following different types and intensities of disturbances that either remove dead coral skeletons (e.g., tropical storms) or leave standing dead skeletons (e.g., coral bleaching) and run associated analyses. We explored the sensitivity of the model to variation in key parameters, such as the strength of herbivory, and the degree to which dead skeletons protect algae from herbivory. Further, we assessed disturbance intensities and values of these parameters that lead to shifts between coral and macroalgae-dominated reefs. This code was published in Ecology and were a part of the thesis of K. Kopecky (2023). Analyses and full methods descriptions of this model can be found in the manuscript “Material legacies can degrade resilience: Structure-retaining disturbances promote regime shifts on coral reefs” (DOI: https://doi.org/10.1002/ecy.4006). No novel data were used or generated in this study. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong></p> <p>Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.</p>
Code, data and scripts to study wave dynamics in asymmetric material
<p>Supplementary data for [1] Vladislav A. Yastrebov. "Wave propagation through an elastically-asymmetric architected material", 2021, https://arxiv.org/abs/1712.06294v2</p> <p>See "Readme.md" and indivual "Readme.md" files in folders: "data", "src", "fig"</p>
Supplementary Materials for "On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective"
<p>Supplementary materials with all used benchmark scripts, plot scripts, benchmark results and PIConGPU example data for the submission to "The 1st International Workshop on Data Reduction for Big Scientific Data (DRBSD-1)" held in conjunction with ISC 2017 in Frankfurt, Germany.</p>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
Supplementary Material for "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City"
<p>This data repository is for the publication "Using Unstructured Crowd-sourced Data to Evaluate Urban Tolerance of Terrestrial Native Animal Species within a California Mega-City" and contains all R scripts and data files to reproduce results as well as all supplementary tables and figures.</p>
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
Training data for 'Upload data to ENA' (Galaxy Training Material)
<p>The data here is a subset of the data published in 10.5281/zenodo.3732359 to be used in GTN 'Upload data to ENA' tutorial.</p> <p>Human traces have been removed following <a href="https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html">https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/human-reads-removal/tutorial.html</a></p> <p>We produced consensus sequences (*.fasta) for the Illumina PE data following SARS-CoV-2-PE-Illumina-WGS-variant-calling (https://workflowhub.eu/workflows/113?version=4), SARS-CoV-2-variation-reporting (https://workflowhub.eu/workflows/109?version=5) and COVID-19-consensus-construction (https://workflowhub.eu/workflows/138?version=4) workflows.</p>
Replication Materials for Disclosure Limitation and Confidentality Protection in Linked Data
<p>These are the data and derived figures as used in the chapter by Abowd, Schmutte, and Vilhuber, "Disclosure Limitation and Confidentiality Protection in Linked Data"</p>
Urban material ground truth data for the 2015 APEX hyperspectral image of Brussels
<p>This dataset entails a spectral library file (.sli file with matching .hdr text file) with 1350 georeferenced and labeled spectra derived from the 2m resolution airborne hyperspectral APEX image of Brussels (Belgium) that was acquired during the summer of 2015. The labeled spectra included in this dataset describe level 2A surface reflectance profiles ranging between 450 and 2431 nm. The original APEX image files can be downloaded via the <a href="https://belair.vito.be/en/belair-data" target="_blank" rel="noopener">Belair website</a>, and the preprocessing performed on this image data is explained in Sterckx et al. (2016) and Vreys et al. (2016). See the "Related works" section of this data publication.</p> <p>The main purpose of this dataset is to provide Ground Truth (GT) data for remote sensing-based mapping experiments with a generic urban spectral library, performed in the frame of the GENLIB research project. The content of this dataset hence focuses on the optical reflectance/absorption behaviour of urban surface materials and their variations.</p> <p>The spectra included in this dataset were manually sampled from the above mentioned APEX image and labeled using ancillary reference data (very high-resolution aerial imagery, Google Street View, LiDAR ...), already published urban spectral libraries, terrain knowledge and some field work. The header of the spectral library contains the various labels that were added to these spectra. These labels cover:</p> <ul> <li>EAGLE Land Cover Component (LCC) from the EAGLE matrix version 3.1. Visit the <a href="https://land.copernicus.eu/en/eagle" target="_blank" rel="noopener">website of the EAGLE framework</a> for more information.</li> <li>Material Groups (MG).</li> <li>Artificial Material Types (AMT).</li> <li>Artificial Material Coating or Fabrication (AMCF).</li> <li>Artificial Material Forms (AMF).</li> <li>Latitude (degrees, WGS84).</li> <li>Longitude (degrees, WGS84).</li> </ul> <p>The value domains of these spectrum attributes are described in the look-up table included as a CSV-file in this data publication.</p> <p>While considerable efforts have been made to safeguard the accuracy of these data, they are published as is, without any warranty or support. Use at your own discretion.</p>
Experimental data generated on the stability of hydrophobic porous materials
<div>/* **********</div> <div>/* This work is licensed under a Creative Commons Attribution 4.0 International License.</div> <div>/* **********</div> <div> </div> <div>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). </div> <div>Underlying data for the publication Amayuelas, E. et al. Bimetallic Zeolitic Imidazole Frameworks for Improved Stability and Performance of Intrusion-Extrusion Energy Applications. The Journal of Physical Chemistry 2023, 127, 18310-18315. https://doi.org/10.1021/acs.jpcc.3c04368. Data related to Figures 2, 3 and 4 in the article.</div> <div> </div> <div>Dataset Identifier: 10.5281/zenodo.11273904</div> <div> </div> <div>Contact person: Eder Amayuelas (CIC energiGUNE). ORCID: </div> <div> </div> <div> </div> <div>The archive 'JPCC_3c04368.zip' contains 25 files:</div> <p> </p>
Aversive imagery causes de novo fear conditioning (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Mueller, E. M., Sperl, M. F. J., & Panitz, C. (2019). Aversive imagery causes de novo fear conditioning. <em>Psychological Science</em>, <em>30</em>(7), 1001–1015.</strong></p> <p>In classical fear conditioning, neutral conditioned stimuli (CS) that have been paired with aversive physical unconditioned stimuli eventually trigger fear responses. Here, we test whether aversive mental images systematically paired with a CS may also cause de novo fear learning in the absence of any external aversive stimulation. In two experiments, <em>N</em>=45 and <em>N</em>=41 participants were first trained to produce aversive, neutral, or no imagery in response to one of three different visual imagery cues. In a subsequent imagery-based differential conditioning paradigm, each of the three cues systematically co-terminated with one of three different neutral faces. Although the face that was paired with the aversive imagery cue was never paired with aversive external stimuli or threat-related instructions, participants rated it as more arousing, unpleasant, and threatening and displayed relative fear bradycardia and fear-potentiated startle. These results could be relevant for the development of fear and related disorders without trauma.</p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Data on robotic grinding of Inconel 718 part with 3M Cubitron II 984F belt for tool wear and material removal analysis
<p>Data on robotic grinding of Inconel 718 part for tool wear and material removal analysis</p> <p>Date data was obtained: May 2019</p> <p>The performance of a metal grinding operation with a robot has been studied, specifically how the grinding capability changes as time goes by and the tool gets worn. A pneumatic grinding tool has been implemented on the robot flange and abrasive belts of 3M Cubitron II 984F have been used.</p> <p>A rectangular metallic part of known dimensions has been attached to a load cell and has been grinded several times in consecutive tests. The grinding operation consists on a straight line along the longest side of the part.<br> During each test session, the same abrasive belt was used with fixed grinding conditions (tool angle, applied force, overlap, robot feed), until the grinding time reached 20 minutes.<br> The metal part has been weighed at regular intervals with the load cell, which allowed us to measure the evolution of the removed height of material for each pass, depending on grinding time.<br> The quantity of grinded material is measured as the height reduction in the part as the robot moves over the part at certain speed.</p> <p>Test sessions were designed for 4 tool angles (25, 35, 45, 75º related to the vertical), and were repeated three times for each angle. With 75º the tool was almost horizontal and it provided the smallest material removal capability. With 25º the tool was almost perpendicular to the area being grinded and it provided the highest material removal capability. </p> <p>The data of the tests is presented in the following units:<br> - Time: seconds:<br> - Removed material height per grinding tool pass: millimeters.</p> <p>The user of the data may easily convert the removed material height per pass into removed material volume or weight per pass. Considering that the width of the grinding belt is 12.5 mm, and the length of the grinded tool is 160 mm, if the height of the removed material is multiplied to the length and width the removed volume per pass can be calculated. Multiplying the volume with the density the weight of the removed material per pass can be calculated.</p> <p>The results of different tests presented in the .xlsx document, which can be accessed using free software such as OpenOffice or LibreOffice:<br> https://www.openoffice.org<br> https://www.libreoffice.org/</p> <p><br> TECHNICAL DESCRIPTION OF THE USED DEVICES AND CONDITIONS</p> <p>- Material of the grinded part: Inconel 718, density 8.19g/cm3. <br> - Dimensions of the grinded part: 160x90x40 mm.<br> - Robot: Stäubli TX90L.<br> - Belt grinding tool: AMTRU SwingBelt 120. https://www.amtru.com<br> - Applied pneumatic pressure on the grinding tool: 9 bars.<br> - Belt speed: Maximum possible speed obtained with 7 bars pneumatic mains in the workshop.<br> - Abrasive belt: 3M Cubitron II 984F, 610x12.5 mm, 36 grit (roughing).<br> - Load cell to measure the weight of the part: HBM SP4M, capacity 3 kg, precision 0.01 g.<br> - Overlap (tool lateral displacement between two passes): 6.25 mm.<br> - Robot feed: 75 mm/s (100 mm/s for the 45º test).</p> <p>IDEKO Research Centre<br> Address: <br> Arriaga kalea 2<br> 20870, Elgoibar, SPAIN<br> Contact:<br> Asier Barrios, abarrios@ideko.es<br> Patxi Hacala, phacala@ideko.es<br> Phone: (+34) 943 74 80 00</p>
Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal (Open Data and Open Materials)
<p><strong>Open Data and Open Materials of: Placebo nasal spray protects female participants from experimentally induced sadness and concomitant changes in autonomic arousal. <em>Journal of Affective Disorders</em>. </strong></p> <p><em>Background:</em> To investigate the powerful placebo effects in antidepressant drug trials and their mechanisms, recent pioneering experimental studies showed that expectation manipulation combined with an active placebo attenuated induced sadness. In the present study, we aimed at extending these findings by assessing the psychophysiological response in addition to mere self-report.</p> <p><em>Methods:</em> One hundred thirteen healthy female students were randomly assigned to a drug expectation group (active placebo, positive treatment expectation), placebo expectation group (active placebo, no treatment expectation), or a no-treatment group (no placebo, no treatment expectation). After placebo intake, sadness was induced by self-deprecating statements using the Velten method combined with sad music, including a rumination phase. Sadness was measured using the Positive and Negative Affect Schedule Expanded Form (PANAS-X). Heart rate and skin conductance were assessed continuously.</p> <p><em>Results:</em> After mood induction and after rumination, self-reported sadness was significantly lower, and skin conductance level was significantly higher, in the drug expectation group than in the no-treatment group. The mood induction was further accompanied by a heart rate deceleration within all groups.</p> <p><em>Limitations: </em>Generalizability is limited by sample selectivity and focusing on sadness as a symptom of depression, exclusively.</p> <p><em>Conclusion:</em> Expectation-induced placebo effects significantly influenced sadness-correlated changes in autonomic arousal, and not only subjectively reported sadness, indicating that placebo effects in the context of affect are not merely due to subjective response bias. The systematic modification of treatment expectation could be utilized in clinical practice to optimize current therapeutic approaches to improve mood regulation.</p>
Si data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.</p> <p>The files included are:</p> <ul> <li><strong>Si.cell:</strong> structure file containing atom locations</li> <li><strong>Si.den_fmt:</strong> electron density data, generated with CASTEP</li> <li><strong>Si.castep:</strong> CASTEP log file for the electron density calculation</li> <li><strong>Si-muairss-uep.yaml:</strong> configuration file for the AIRSS / UEP workflow</li> </ul>
Research data for Investigation of coatings and metallic materials for icephobic properties, dataset
<p>This dataset is used in deliverable 3.5, 'Investigation of coatings and metallic materials for icephobic properties', where you can get more information.</p> <p>The Dataset includes:</p> <table> <tbody> <tr> <td>Coating Data</td> </tr> <tr> <td>Metalic materials data</td> </tr> <tr> <td>Coating Freezing spike</td> </tr> <tr> <td>Coating Atmospheric freezing</td> </tr> <tr> <td>Coating contact angle</td> </tr> <tr> <td>Coating Ice adhesion</td> </tr> <tr> <td>Submerged freeze depression</td> </tr> <tr> <td>Metalic materials droplet freezing</td> </tr> <tr> <td>Metalic materials Droplet contact angle</td> </tr> <tr> <td>Coating freeze depression brine test</td> </tr> <tr> <td>Coating freeze depression CFT</td> </tr> <tr> <td>Metalic amorphous materials freeze depression</td> </tr> <tr> <td>Metalic pure materials freeze depression</td> </tr> </tbody> </table>
Data set for the journal article: Colloidal-ALD Grown Metal Oxide Shells Enable the Synthesis of Photoactive Ligand/ Nanocrystal Composite Materials
<p>The data for each figure of the main manuscript is included in this folder.</p> <p>Figure 1 is not included as it contains no data.</p> <p>The folder for Figure 2 contains a sub-folder for the EDX and NMR data of 9-ACA/PbS@AlOx. The NMR data was processed by Mestrenova.</p> <p>The folder for Figure 3 contains optical absorption spectrum data of 9-ACA/PbS@AlOx.</p> <p>The folder for Figure 4 contains NMR data which was processed by Mestrenova. It contains the data for 9-ACA/CuInS2@AlOx, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx.</p> <p>The folder for Figure 5 is made of three sub-folders for figure 5A, 5B and 5C. 5A and 5B contain optical absorption for the CuInS2 and CsPbBr3 datasets while 5C contain time resolved data for CsPbBr3.</p> <p>The folder for Figure 6 contains time resolved PL for the as synthesized CsPbBr3, 1-PCA/CsPbBr3@AlOx and 9-PTA/CsPbBr3@AlOx. The 9-PTA/CsPbBr3@AlOx data contain two decays that span 200 ns (short) or 13.5 us (long).</p> <p>The folder for Figure 7 contains time resolved PL for the as synthesized 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx. For both samples the data contain two decays that span 200 ns (short) or 13.5 us (long). Also an NMR folder is present with the 1H spectrum for 9-PTA/CsPbBr3@AlOx and 1-PCA/9-PTA/CsPbBr3@AlOx.</p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.