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9,882 results for “Scale”
Input data for the paper "Assessing the viability of CO2 storage in offshore formations of the Gulf of Mexico at a scale relevant for climate-change mitigation"
<p>This repository contains the input data necessary to reproduce the modeling results shown in the paper "Assessing the viability of CO2 storage in offshore formations of the Gulf of Mexico at a scale relevant for climate-change mitigation", published at the International Journal of Greenhouse Gas Control journal in May 2023.</p>
Dataset of the Distress Screening Scale for the article: Emotional distress, coping, and social support: A network analysis of risk and resources in persons with cancer
<p>Dataset of the Distress Screening System comprises 52 items that relate to six psycho-social domains of human functioning: depression, anxiety, social support, coping efficacy, satisfaction with care, and functional status</p>
MIntRec2. 0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
<p><span>MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability.</span> <span>This dataset will be released under the CC BY-NC-SA 4.0 license.</span></p>
A large scale photonic matrix processor enabled by charge accumulation
<p>Integrated neuromorphic photonic circuits aim to power complex artificial neural networks (ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low loss of photonic structures. However, scaling photonic circuits to match the requirements of modern ANNs still remains challenging. In this perspective, we give an overview over the usual sizes of matrices processed in ANNs and compare them with the capability of existing photonic matrix processors. To address shortcomings of existing architectures, we propose a time multiplexed matrix processing scheme which virtually increases the size of a physical photonic crossbar array without requiring any additional electrical post-processing. We investigate the underlying process of time multiplexed incoherent optical accumulation and achieve accumulation accuracy of 98.9% with 1 ns pulses. Assuming state of the art active components and a reasonable crossbar array size, this processor architecture would enable matrix vector multiplications with 16,000 × 64 matrices all optically on an estimated area of 51.2 mm<sup>2</sup>, while performing more than 110 trillion multiply and accumulate operations per second.</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres
<p>XRD-CT data presented in the manuscript: "A multi-scale study of 3D printed Co-Al2O3 catalyst monoliths versus spheres" by Clement Jacquot et al. The h5 files contained the integrated and reshaped diffraction datas as well as a native 2theta x axis.</p>
Fig. 7 in The coccid-tending ant genus Acropyga Roger and its obligate associated myrmecophilous scale insect genus Eumyrmococcus Silvestri new to Italy (Hymenoptera: Formicidae; Hemiptera: Xenococcidae)
Fig. 7 – Subgenital plate of adult male Acropyga paleartica.
Fig. 4 in The coccid-tending ant genus Acropyga Roger and its obligate associated myrmecophilous scale insect genus Eumyrmococcus Silvestri new to Italy (Hymenoptera: Formicidae; Hemiptera: Xenococcidae)
Fig. 4 – Acropyga paleartica, gyne: head, frontal view (Scale bar: 0,5 mm).
Map of Kusunda migratory route and present settlements in Nepal (grey-scale version)
<p>This map shows the approximate migratory route that the two remaining bands of Kusunda people took around the turn and into the first quarter of the 20th century, with the center in Balkōṭ, formerly in Gulmī district, now in Arghākhām̐cī. As the members of the exogamous Kusunda clans in these bands did not find suitable partners to marry, they had to marry sedentary partners of other ethnic and caste groups. Gradually, all Kusunda settled down, and the present Kusunda population in the various districts of western Nepal still reflect the old migration pattern. </p> <p>This map was created by Yeshy T. Sotrug based on analysis by Timotheus A. Bodt and Uday Raj Aaley of information provided by the remaining Kusunda elders between 2018 and 2022. </p> <p>A colour version map exists on 10.5281/zenodo.8175479</p>
Additional information for Biofilm colonization and succession in a full-scale partial nitritation-anammox moving bed biofilm reactor
<p>Here we include additional files for Suarez <em>et al</em>. <strong>Biofilm colonization and succession in a full-scale partial nitritation-anammox moving bed biofilm reactor</strong></p> <p>Files included here:</p> <ul> <li><strong>MAG_annotations_EUK.tsv</strong>. Genome annotation of eukaryotic MAGs made with eggnog-mapper v2.1.9. </li> <li><strong>MAG_annotations_PROK.tsv</strong>. Genome annotation of bacterial and Archaeal MAGs made with eggNOG-mapper v2.1.9</li> </ul> <p>Metagenome reads and MAGs have been deposited at the European Nucleotide Archive (ENA) under accession PRJEB58181.</p>
Optimisation of size and control strategy in utility-scale green hydrogen production systems
<p>Data for the simulation of Fig. 14 of the paper Optimisation of size and control strategy in utility-scale green hydrogen production systems</p>
Research data for "Device-scale atomistic modelling of phase-change memory materials"
<p>This is a dataset related to the publication "Device-scale atomistic modelling of phase-change memory materials".</p> <p>Two folders have been provided for (1) the production data shown in this work, and (2) GAP models trained in this work:</p> <p>(1) The production data have been categorised according to the main text figures:</p> <ul> <li>"reference_database": Reference databases (i.e., training structures) of three GAP models discussed in this work. The structure data are provided in (extended) XYZ format, as labelled using either the PBEsol or the PBE functional. <ul> <li>"main_GST-GAP-22_PBEsol": the main GST-GAP-22 database, which was fitted using a two-step iterative training protocol. The resulting GAP model was used to obtain the results shown in the main text. The reference database is visualised in Fig. 1 of the main text. </li> <li>"refitted_GST-GAP-22_PBE": this dataset contains the same structures as the original GST-GAP-22 training data, with all structures having been relabelled using the PBE functional.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": an extension of the GST-GAP-22 database to a new, task-specific application, i.e., electromigration under an external electric field (cf. Extended Data Fig. 4).</li> </ul> </li> </ul> <ul> <li>"crystallization_simulations": three trajectories for the crystallization simulations shown in Fig. 2, of which all were obtained from GAP-MD. <ul> <li>"fig2b_growth_GAP-MD": Growth of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2c_cumulative_set_cycles_GAP-MD": Cumulative set process of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2d_crystallization_12096at_GAP-MD": Crystallization of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (12096 atoms), in which three crystalline seeds were used.</li> </ul> </li> </ul> <ul> <li>"RESET_mushroom_model": two non-isothermal simulations shown in Fig. 3, obtained from GAP-MD. <ul> <li>"Fig3b_small_pulse": The 70 ps NVE equilibrium process after a small heating pulse was imposed in the focal area, giving an excess kinetic energy of 1,650 eV for the atoms in the focal area.</li> <li>"Fig3d_large_pulse": The 70 ps NVE equilibrium process after a large heating pulse was imposed in the focal area, giving an excess kinetic energy of 3,900 eV for the atoms in the focal area.</li> </ul> </li> </ul> <ul> <li>"RESET_device_scale_simulations": the GAP-MD simulations of the melting and heat dissipation process of a device-scale structural model shown in Fig. 4. <ul> <li>"Fig4b_device_scale<strong>_</strong>heating_10ps": The melting process of the device-scale model over 10 ps.</li> <li>"Fig4c_device_scale<strong>_</strong>cooling_40ps": The heat dissipation process of the device-scale model over another 40 ps.</li> </ul> </li> </ul> <p>(2) The GAP models trained in this work:</p> <ul> <li>"main_GAP_potential": the main GAP model used for the production data of this work, which is fitted based on PBEsol data. The XML identifier of this GAP model is GAP_2022_4_7_480_18_6_12_970.</li> </ul> <ul> <li>"other_GAP_potentials": two derivatives of the original GAP model. <ul> <li>"refitted_GST-GAP-22_PBE": using the same reference structures as the original GAP model but re-labelled using the PBE functional. The XML identifier of this GAP model is GAP_2022_5_7_480_0_58_2_26.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": An extension of the original GAP model to a new, task-specific application, i.e., electromigration under an external electric field. The XML identifier of this GAP model is GAP_2023_3_19_480_18_14_10_174.</li> </ul> </li> </ul> <p> </p>
Data for "Negative effects of wind on plant hydraulics at the global scale"
<p>To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.</p> <p>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.</p>
34 Block--63k or 50k scale--Maps of South Asia
<p>A set of 122 maps by the Survey of India covering Block 34. </p> <p>In the time since this first batch of maps (see the list below) was posted additional maps of Block 34 have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an in Index Map showing the total 63k/50k holdings in Block 34.</p> <table> <tbody> <tr> <td><a href="../records/10456241">63k/50k Maps of South Asia--Block 34--additional--20240103</a></td> </tr> </tbody> </table>
Experimental HR-TEM Images of GO samples at 5nm scale
<p>Experimental HR-TEM micrograph in the GO sample, prepared at the highest carbonization temperature (TCA) of 973 K, with 5.3% of oxides coverage estimated experimentally via XPS [42] and taken at a 5-nm scale.</p>
Mountain Scale Armor
Lowpoly Mountain Scale armor for Normalmap bakes etc. Use it as you please. This is not the best but can be used for quick conecpting. Feel free to alter it further for better result. Source: Objaverse 1.0 / Sketchfab
Weighing Scales
3D model of general-purpose scales made by Salter Housewares Ltd. Original object dated late 19th century. Physical object held at the Timespan Museum. Part of the Helmsdale Fishing Village reconstruction. A collaborative project between Open Virtual Worlds, a research team within the School of Computer Science, University of St Andrews and Timespan Museum. 3D digitisation was done by Catherine Cassidy and Iain Oliver with archaeological assistance provided by Jacquie Aitken. Digitisation funded by the EU Northern Periphery and Arctic Programme 2014-2020 through the "Connected Culture and Natural Heritage in a Northern Environment" (CINE) project. Source: Objaverse 1.0 / Sketchfab
Test Lithic 20200614 Focus-Stacked and Scaled
2.75 cm tall, scaled, focus-stacked, lithic. From personal collection. Source: Objaverse 1.0 / Sketchfab
KaJaPa [3D scan from scale model] V2
This 3D model is a reconstruction generated with photogrammetry software 3DF Zephyr v4.519 processing 538 images taken from a 1/35 scale model. # **This model is to be used as reference when making a 3D model of this tank.** The Kanonenjagdpanzer (KanJPz; also known as Jagdpanzer Kanone 90mm, or tank destroyer, gun) was a German Cold War tank destroyer equipped with a 90mm anti-tank gun from obsolete M47 Patton tanks. An Image of a early version of the KaJaPa.  The first prototypes of the Kanonenjagdpanzer were built in 1960 by Hanomag and Henschel for West Germany and by MOWAG for Switzerland. Between 1966 and 1967, 770 units were built for the Bundesweh. Eighty of them were delivered to Belgium Due to the increasing armor of eastern tanks Germany decided to convert KaJaPa's into RaJaPa's (Raketen Jagdpanzer) which where fitted with AT-Missiles and some into unarmed observation vehicles. See Wikipedia for more info and images. Source: Objaverse 1.0 / Sketchfab
Data from the Psychometric validation of the SOLARIS Perception Scale
<p><span><span>Our </span><span>main </span><span>aim </span><span>was </span><span>to </span><span>test </span><span>the </span><span>psychometric </span><span>properties </span><span>of </span><span>the </span><span>Perceived </span><span>Deepfake </span><span>Trustworthiness </span><br><span>Questionnaire (PDTQ) across three languages, namely English, Italian, and Slovenian. The PDTQ was </span><br><span>comprehensive of 31 items</span><span>, which pertain to four domains, namely the content of the video, the person in the </span><br><span>video, the source of the video, and other video characteristic. <span>With its validation procedure, we were interested </span><br><span>in the factorial structure of the questionnaire, the measu</span><span>rement equivalence of the three language versions, and </span><br><span>the internal reliability of the questionnaire or dimensions that may emerge.</span></span></span></p> <p><span><span> The dataset contains responses from the validation study. No identifiable information is given. <br></span><br></span></p>
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.