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12,393 results for “materiality”
Fig. 4. – Living material. A. Lijndenia darainensis R.D in Revised treatment of the genus Lijndenia (Melastomataceae, Olisbeoideae) in Madagascar
Fig. 4. – Living material. A. Lijndenia darainensis R.D. Stone, flowering branchlets; B. Lijndenia darainensis R.D. Stone, flowers; C. Lijndenia melastomoides (Naudin) Jacq.-Fél., flowers.
Supplementary material - Eggerthella lenta DSM 2243 alleviates bile acid stress response in Clostridium ramosum and Anaerostipes caccae by transformation of bile acids
<p>The word document is a collection of supplementary figures and tables. <br> The excel file is a collection of raw data from experiments.</p>
High-resolution maps of material stock, population and employment in Austria from 1985 to 2018
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642, data: https://zenodo.org/record/4522892), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>material stock in buildings <ul> <li>in commercial and industrial buildings</li> <li>in multi-family residential buildings</li> <li>in high-rise buildings</li> <li>in single-family residential buildings</li> <li>in lightweight buildings</li> </ul> </li> <li>material stock in road infrastructure</li> <li>material stock in rail infrastructure</li> <li>material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website </a>to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>
SSHOC Training Material Video 3 - Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer - training video in English, French and Spanish
<p>Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer</p> <p>A training video targeting COVID-19 survey producers to entice contributions to the COVID-19 collection of the EMM Survey Registry<br> <strong>Target Audience for the video</strong>: Survey producers (academic and non-academic) of COVID-19 surveys with EMM respondents</p>
SSHOC Training Material Video 2 - How to exploit the Ethnic and Migrant Minorities Survey Registry as an academic and non-academic user - training video in English, French and Spanish
<p>A training video about how to search for and learn about surveys using the EMM Survey Registry, both as an academic and non-academic user.</p> <p>This video has been produced in English, French, Spanish</p>
Materials for "GRB221009A: INTEGRAL detection of Hard X-ray emission at least 38 hours after trigger"
<p>Images and data for "GRB221009A: INTEGRAL detection of Hard X-ray emission at least 38 hours after trigger". Text of the <a href="https://www.astronomerstelegram.org/?read=15663">ATel #15663</a> and <a href="https://gcn.gsfc.nasa.gov/gcn3/32691.gcn3">GCN #32691</a> below.</p> <p> </p> <pre>Volodymyr Savchenko (UNIGE, EPFL), Carlo Ferrigno, Enrico Bozzo (UNIGE), D. Gotz (CEA Paris Saclay), S. Mereghetti (INAF/IASF Milano), Antonio Martin Carrillo, Lorraine Hanlon (UCD), Elisabeth Jourdain, Jean-Pierre Roques (IRAP), Thomas Siegert (University of Würzburg), Erik Kuulkers, Celia Sanchez (ESA) Following the detection of the record-breaking GRB221009A by Swift/BAT (GCN #32632,#32635), Fermi/GBM (GCN #32636), INTEGRAL/SPI-ACS (GCN #32660), we have performed INTEGRAL pointed observations of the GRB221009A location. INTEGRAL pointed observation lasted from 2022-10-10T14:31:40 (T0 + 25.2 hours, where T0 is 2022-10-09T13:17:00) to 2022-10-11T03:18:20 (T0 + 38.0 hours) with a total exposure time of 30.2 ks (for ISGRI). In the complete observation, the source is clearly detected in JEM-X1, JEM-X2 (3-30 keV), and ISGRI (28 - 80 keV), with S/N of 27.8, 27.3, 15.0 respectively. The joint JEM-X and ISGRI spectra can be satisfactorily modeled between 3 and 80 keV with a single powerlaw of slope 2.15 +/- -0.07 (90% confidence) with a flux of 4.4e-10 +/- -2.1e-11 erg/cm2/s (3 - 80 keV). This might indicate a single spectral component spanning from from hard X-ray to Fermi/LAT (GCN #32658). Combination of bright Hard X-ray afterglow with gamma-ray emission was also found in GRB120711A (Martin-Carrillo et al. 2014 A&A 567, 84) and GRB130427A (Kouveliotou et al. 2013 ApJ 779L, 1K) - in fact GRB221009A appears rather similar to GRB120711A, but at 10 times smaller distance. Within the relatively short JEM-X not ISGRI lightcurves, we do not observe any evidence for flux decrease. Further INTEGRAL observations are scheduled between 2022-10-11 13:52:21 and 2022-10-13 00:58:26. These observations will overlap with the planned observation of IXPE (GCN #32690). We are grateful to the INTEGRAL Ground Segment team for quickly scheduling the observations. Images and reduced data related to this publication can be found here: https://zenodo.org/record/7186289 </pre>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
CONVERSE 2022 Distributed Volcanism Scenario Exercise materials
<p>The CONVERSE research coordination network, aimed at organizing the US volcano science community towards better organization and collaboration, ran an eruption scenario exercise in February 2022. The exercise simulated an unrest and eruption event in a distributed volcanic field in the southwestern US (Arizona). During the activity, the organizers shared synthetic and re-purposed data and "official" information statements with the participants. Data types included seismic, geodetic (GPS / InSAR), gas, remote-sensing, and imagery. </p> <p>This dataset accompanies the publication "Lessons Learned from the 2022 CONVERSE Monogenetic Volcanism Response Scenario Exercise", by Yolanda Lin et al. </p>
Mechanics of polymeric materials in context
<p><strong>Mechanics of polymeric materials in context</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Mechanics is the science concerned with the motion of bodies under the action of forces, including the special case in which a body remains at rest. Of first concern in the problem of motion are the forces that bodies exert on one another. This leads to the study of such topics as gravity, electricity, and magnetism, according to the nature of the forces involved. Given the forces, one can seek the manner in which bodies move under the action of forces; this is the subject matter of mechanics proper. Mechanics may be divided into three branches: statics, which deals with forces acting on and in a body at rest; kinematics, which describes the possible motions of a body or system of bodies; and kinetics, which attempts to explain or predict the motion that will occur in a given situation. Alternatively, mechanics may be divided according to the kind of system studied. The simplest mechanical system is the particle, defined as a body so small that its shape and internal structure are of no consequence in the given problem. More complicated is the motion of a system of two or more particles that exert forces on one another and possibly undergo forces exerted by bodies outside of the system. The central concepts in classical mechanics are force, mass, and motion. Neither force nor mass is very clearly defined by Newton, and both have been the subject of much philosophical speculation since Newton. Both of them are best known by their effects. Mass is a measure of the tendency of a body to resist changes in its state of motion. Forces, on the other hand, accelerate bodies, which is to say, they change the state of motion of bodies to which they are applied. The interplay of these effects is the principal theme of classical mechanics. Although Newton's laws focus attention on force and mass, three other quantities take on special importance because their total amount never changes. These three quantities are energy, momentum, and angular momentum. Any one of these can be shifted from one body or system of bodies to another. In addition, energy may change form while associated with a single system, appearing as kinetic energy, the energy of motion; potential energy, the energy of position; heat, or internal energy, associated with the random motions of the atoms or molecules composing any real body; or any combination of the three. Nevertheless, energy, momentum, and angular momentum are conserved. These three conservation laws arise out of Newton's laws, but Newton himself did not express them. They had to be discovered later. It is a remarkable fact that, although Newton's laws are no longer considered to be fundamental, nor even exactly correct, the three conservation laws derived from Newton's laws, the conservation of energy, momentum, and angular momentum, remain exactly true even in quantum mechanics and relativity. In fact, in modern physics, force is no longer a central concept, and mass is only one of a number of attributes of matter. Energy, momentum, and angular momentum, however, still firmly hold center stage. The continuing importance of these ideas inherited from classical mechanics may help to explain why this subject retains such great importance in science today. Statics is the study of bodies and structures that are in equilibrium. For a body to be in equilibrium, there must be no net force acting on it. In addition, there must be no net torque acting on it. When a body has a net force and a net torque acting on it owing to a combination of forces, all the forces acting on the body may be replaced by a single force called the resultant, which acts at a single point on the body, producing the same net force and the same net torque. The body can be brought into equilibrium by applying to it a real force at the same point, equal and opposite to the resultant. This force is called the equilibrant.</p>
Dataset for "Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR"
<p>This dataset constitutes the pre-processed data contained in the publication 'Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR' Analytical Chemistry (2022) 94(14):5566-5574 (DOI: 10.1021/acs.analchem.1c05134).</p>
PCEDNet - Replicability material
<p>Material required to replicate the paper:</p> <blockquote> <p>Chems-Eddine Himeur, Thibault Lejemble, Thomas Pellegrini, Mathias Paulin, Loic Barthe, and Nicolas Mellado. 2021. <br> PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds. <br> ACM Trans. Graph. 41, 1, Article 10 (February 2022), 21 pages. <br> DOI:https://doi.org/10.1145/3481804</p> </blockquote> <p>Includes the following files:</p> <ul> <li>networks.zip: pre-trained networks,</li> <li>default.zip: dataset provided by the authors, including Ground Truth labels (see paper for more details)</li> <li>abc.zip: dataset generated from the ABC dataset, including Ground Truth labels (see paper for more details)</li> <li>point-clouds.zip: point-clouds without Ground Truth (see paper for more details) </li> </ul> <p> </p>
Video material for: "Immersive audio inside the Greek-Roman theatre of Tyndaris: comparison between past, current and future conditions"
<p>The two folders contain 24 spherical videos with 3OA audio used for the subjective listening tests described in the paper:<br> Lavagna L, Shtrepi L, Farina A, Bevilacqua A, Astolfi A. <em>Immersive audio inside the Greek-Roman theatre of Tyndaris: comparison between past, current and future conditions</em> In: Proceedings of the 24th International Congress on Acoustics. Gyeongju; 2022.<br> <br> The listening test, structured as an AB comparison, were presented inside the virtual reality environment using a Head Mounted Display (HMD), specifically a Meta Quest 2, and open headphones (Sennheiser HD 650).<br> <br> </p>
Data supplement to "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas"
<p>Supplementary data and codes for "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas". Zipped files contain ANUGA hydrodynamic model outputs, dorado particle-routing simulation outputs, Python scripts for running additional dorado simulations, and other metadata used in the analysis of dorado outputs. See README for additional details about directory contents. Note that this directory does not contain the model software itself, which is available on GitHub and has been archived elsewhere (relevant links can be found in README).</p>
Composition and electrical resistance results of a Ir-Pd-Pt-Rh-Ru composition spread thin film materials library
<p>The dataset contains the results of electrical resistance measurement and composition analysis of a thin film composition spread materials library. </p> <p>342 measurement areas were evaluated for chemical composition using energy dispersive X-ray spectroscopy and electrical resistance using a 4-point probe.</p> <p>CSV columns:</p> <p>x: x-coordinate of materials library in µm</p> <p>y: y-coordinate of materials library in µm</p> <p>Ir: relative chemical composition in at.%</p> <p>Pd: relative chemical composition in at.%</p> <p>Pt: relative chemical composition in at.%</p> <p>Rh: relative chemical composition in at.%</p> <p>Ru: relative chemical composition in at.%</p> <p>Resistance: electrical resistance in Ohm</p> <p> </p> <p>This dataset is supplementary information for an associated publication. A link to the publication will be provided after publishing.</p>
Training data for ChIP-seq data analysis (Galaxy Training Material): Identification of the binding sites of the Estrogen receptor
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes ChIP-seq data from a study published by Ross-Inness et al., 2012 (DOI:10.1038/nature10730) to identify the binding sites of the Estrogen receptor, a transcription factor known to be associated with different types of breast cancer.</p>
Supplementary material from: Aslan CE, Sikes BA, Gedan KB (2015) Research on mutualisms between native and non-native partners can contribute critical ecological insights. NeoBiota 26: 39-54. https://doi.org/10.3897/neobiota.26.8837
Supplementary material from: Aslan CE, Sikes BA, Gedan KB (2015) Research on mutualisms between native and non-native partners can contribute critical ecological insights. NeoBiota 26: 39-54. https://doi.org/10.3897/neobiota.26.8837
Supplementary material 2 from: Steinberg E, Nieves M, Mudry M (2014) Multiple sex chromosome systems in howler monkeys (Platyrrhini, Alouatta). Comparative Cytogenetics 8(1): 43-69. https://doi.org/10.3897/compcytogen.v8i1.6716
Supplementary Figure S. (doi: 10.3897/CompCytogen.v8i1.6716.app2) File format: Microsoft Word file (doc).:
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
Supplementary material 2 from: Trochet A, Schmeller D (2013) Effectiveness of the Natura 2000 network to cover threatened species. Nature Conservation 4: 35-53. https://doi.org/10.3897/natureconservation.4.3626
Supplementary material 1 from: Gruber B, Evans D, Henle K, Bauch B, Schmeller D, Dziock F, Henry P, Lengyel S, Margules C, Dormann C (2012) "Mind the gap!" – How well does Natura 2000 cover species of European interest? Nature Conservation 3: 45-62. https://doi.org/10.3897/natureconservation.3.3732
The table shows the 54 gap species ordered by species group. Please note: The comment column is based on expert opinion of the European Topic Centre, which has the latest version of the Natura2000 data base and also knowledge on confidential sites, which are deleted from the public version of the data base to protect rare species.
Supplementary material 1 from: Schmeller D, Maier A, Evans D, Henle K (2012) National responsibilities for conserving habitats – a freely scalable method. Nature Conservation 3: 21-44. https://doi.org/10.3897/natureconservation.3.3710
The annex does provide a more detailed comparison of the different biogeographic maps. It further gives a list of all forest habitats given in the EU Habitats Direction
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.