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9,882 results for “Scale”
Datasets produced by SCALE-GM in QBO-like oscillation experiments.
<p>Data produced by SCALE-GM and used in the manuscript submitted to the AMS journal Monthly Weather Review.</p> <p>Authors: <br> Hiroki Kashimura, Hisashi Yashiro, Seiya Nishizawa, Hirofumi Tomita, Kensuke Nakajima, Masaki Ishiwatari, Yoshiyuki O. Takahashi, and Yoshi-Yuki Hayashi<br> Corresponding Author:<br> Hiroki Kashimura (hiroki@gfd-dennou.org)<br> Manuscript Title:<br> Dependence of a QBO-like oscillation on time-step intervals in a non-hydrostatic general circulation model</p> <p>See README.txt for more details.</p> <p> </p>
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
<p><strong>AIS data collected by the University of Piraeus' AIS receiver</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9<sup>th</sup>, 2017 to December 26<sup>th</sup>, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.</p> <p> </p> <p><strong>Dataset related to the following publication</strong></p> <blockquote> <p>Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2021.107782">https://doi.org/10.1016/j.dib.2021.107782</a>.</p> </blockquote> <p> </p> <p><strong>Files Description</strong></p> <ul> </ul> <ul> <li><strong>ais_static</strong>: CSV flat files containing vessels' static information and their corresponding types</li> </ul> <ul> <li><strong>geodata</strong>: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)</li> </ul> <ul> <li><strong>noaa_weather</strong>: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)</li> </ul> <ul> <li><strong>unipi_ais_dynamic</strong>: CSV flat files containing AIS kinematic information </li> </ul> <ul> <li><strong>unipi_ais_dynamic_synopses</strong>: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions</li> </ul> <p> </p> <p><strong>Privacy Statement</strong></p> <p><strong>For privacy-related queries, please contact the authors</strong></p>
A Chromosome-Scale Genome of the Korean Cultivar Sesamum indicum var. Goenbaek
<p>Supporting data set of the Korean sesame cultivar, Sesamum indicum var. Goenbaek genome. For any inquiry, please contact Keunpyo Lee Ph.D. at kplee@korea.kr</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
SCALE-WIN19 SDS
<p>The files here contain the sensor data from the RV SA Agulhas II scientific data system (SDS). Please note that the salinity data has been calibrated. The files are given in a 1 minute and 10 minute resolution.</p> <p>For notes on how the data was processed please refer to the SCALE Cruise Report.</p>
SCALE-SPR19 SDS
<p>The files here contain the sensor data from the RV SA Agulhas II scientific data system (SDS). Please note that the salinity data has been calibrated. The files are given in a 1 minute and 10 minute resolution.</p> <p>For notes on how the data was processed please refer to the SCALE Cruise Report.</p>
Datasets from Ganuza et al. 2022: Interactive effects of climate and land use on pollinator diversity differ among taxa and scales
<p>Datasets used in Ganuza et al. 2022: Interactive effects of climate and land use on pollinator diversity differ among taxa and scales. Local and regional data are provided in separate files for the environmental variables, plant species composition and the composition of the different pollinator taxa.</p>
Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"
<p>Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity".</p> <p>Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)</p>
Small-scale Concentrated Solar Power (CSP): Challenges & Opportunities
<p>☀️Interested in Concentrated Solar Power technologies?</p> <p>💻Watch the replay of this 1-hour webinar that will give you key information on the challenges and opportunities offered by CSP and the application at small-scale.</p> <p>ℹ️For more information click here <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0NOeFFBczA0Qm52SGhKM2ptWU5laEVtQ3k0UXxBQ3Jtc0tsUGpYT3MtTmZrdFdQb0JjMjVKQ09zTldpQThmVHQtVjU3cjJ6S0JhNlA4TW1pM3AxNGJPLXBZQVJramZ4Uk9XQXJGWWZLZ0RiRkxvN1dSNVFjQWx6cHFJOS1fVzN0QTRrY19VTVZoY3lYbFVLN0hlaw&q=https%3A%2F%2Fwww.polyphem-project.eu%2F2021%2F11%2F15%2Fwebinar-small-scale-csp-challenges-opportunities%2F">https://www.polyphem-project.eu/2021/...</a></p> <p>🔎Join our community!<br> Official website <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbU9JdlNfeTZ4WHktZnhuMERaenROTVNfT3o5UXxBQ3Jtc0tuMXIzTFMtS0pDZGlEZnA0NEd3RlJWNGRWVVBnZlJNQzlYdmh2MGRBZXFBVW5od1o2MkFOWXI3NHRVZksxNWtxUndxUmdzMTVidVNRdl9JODAtbjVRZ0k0RTkzMFdaQXRWR0hjaWd3MkZhcDJZWnV6MA&q=https%3A%2F%2Fwww.polyphem-project.eu%2F">https://www.polyphem-project.eu/</a><br> Linkedin <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqazJuNHVob3RuaUU4ZmFTWlJjcG52dmFqRjEzd3xBQ3Jtc0tsU2lJaHVaQXlPLVhBWEp4WEVxeWxCc3l1WUFBVVFZMk5vQllkdXNleTdodWdkU2kxV0pDTlNlT0pJSnN5d0wzbnFVU3JlcXNSWnZEekZ6Q2VZRjJSeVNnTHI1aFpZZ0lOUFJIako4SHBkYkdPajNlMA&q=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpolyphem%2F">https://www.linkedin.com/company/poly...</a><br> Twitter <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa05nanVGWDJ5ZDBqVnB2SVgtSEltMy1iNXF6UXxBQ3Jtc0trb3p5WTRWcVpFTTNYeGlkdkY5MjgxZGRUNTZNOUdLbDk1T2Q2a0NMRDFHaEwzWktseHpYSGxPdjJMTG9FdEJXWjYwcjhWYWJMc0Y2cW8zOGpPd1R1eVUzRWFUYXYzblh4S25GZVZ0WGNuQWZ0cDk3cw&q=https%3A%2F%2Ftwitter.com%2FH2020CSP">https://twitter.com/H2020CSP</a><br> <br> 🇪🇺 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 764048.</p>
Earthquakes analyzed through the Environmental Seismic Intensity (ESI) scale
<p>The spreadsheet includes information on over 150 earthquakes that have been analyzed using the Environmental Seismic Intensity scale.</p>
Stability of rocky intertidal communities in response to species removal varies across spatial scales
<p>Improving our understanding of stability across spatial scales is crucial in the current scenario of biodiversity loss. Still, most empirical studies of stability target small scales. Here we experimentally removed the local space-dominant species (macroalgae, barnacles, or mussels) at eight sites spanning more than 1000 km of coastline in north- and south-central Chile, and quantified the relationship between area (the number of aggregated sites) and stability in aggregate community variables (total cover) and taxonomic composition. Resistance, recovery, and invariability increased nonlinearly with area in both functional and compositional domains. Yet, the functioning of larger areas achieved a better, albeit still incomplete, recovery than composition. Compared with controls, smaller disturbed areas tended to overcompensate in terms of total cover. These effects were related to enhanced available space for recruitment (resulting from the removal of the dominant species), and to increasing beta diversity and decaying community-level spatial synchrony (resulting from increasing area). This study provides experimental evidence for the pivotal role of spatial scale in the ability of ecosystems to resist and recover from chronic disturbances. This knowledge can inform further ecosystem restoration and conservation policies.</p>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity
<p> </p> <p><strong>Short description</strong></p> <p>This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology.</p> <p>For further details please see the original article and its supplementary materials.</p> <p> </p> <p><strong>Organization of the data</strong></p> <p>The repository contains two main folders:</p> <p> 1. Target indicators & spatial analysis</p> <p><em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay).</p> <p><em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm.</p> <p><em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site.</p> <p><em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework.</p> <p> 2. CoMOLA input & parameterisation</p> <p>The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA</p> <p><em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings)</p> <p><em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file.</p> <p><em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.</p> <p> </p>
Spatial Scaling Challenge. COST Action CA17134 SENSECO. Working Group 1
<p>This dataset contains the data, documentation, and scripts that compose the <strong>SPATIAL SCALING CHALLENGE</strong> organized in the framework of the <strong>SENSECO COST Action CA17143</strong> “Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits” (<a href="https://www.senseco.eu/">https://www.senseco.eu/</a>), by the <strong>Working Group 1</strong><strong>.</strong> “Closing the scaling gap: from leaf measurements to satellite images” (<a href="https://www.senseco.eu/working-groups/wg1-scaling-gap/">https://www.senseco.eu/working-groups/wg1-scaling-gap/</a>).</p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> is an open exercise where we challenge the remote sensing community to retrieve relevant vegetation biophysical and physiological variables such as leaf chlorophyll content (<em>C</em><sub>ab</sub>), leaf area index (<em>LAI</em>), maximal carboxylation rate (V<sub>c</sub><sub>max,25</sub>), and non-photochemical quenching (<em>NPQ</em>) from simulated (hyperspectral reflectance (<em>HDRF</em>), sun-induced chlorophyll fluorescence (<em>F</em>) and land surface temperature (<em>LST)</em>) imagery.</p> <p>The dataset contains the simulated remote sensing and field data, their description, and scripts in Matlab, Python, and R languages to facilitate importing and handling the data and producing the standardized outputs necessary to participate.</p> <p><strong>IMPORTANT:</strong> <strong>Additional data</strong> that can be used at the discretion of the participants have been released in <strong><a href="https://doi.org/10.5281/zenodo.6530187">https://doi.org/10.5281/zenodo.6530187</a></strong></p> <p>The <strong>SPATIAL SCALING CHALLENGE</strong> aims at gathering the community’s expertise and knowledge to tackle the scaling problems posed by variables of different nature. These experiences will be summarized in a journal article where all the participants are invited to contribute. The exercise is internationally open. Ph.D. students, early career and senior researchers, spin-offs, and companies working in the field of remote sensing of vegetation ecophysiology are welcome to participate.</p> <p><strong>STILL OPEN FOR PARTICIPATION! New deadline 31<sup>st</sup> of October 2022.</strong></p> <p>Follow all the <strong>communications and updates</strong> of the <strong>SPATIAL SCALING CHALLENGE</strong> in the RG site: <strong><a href="https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1">https://www.researchgate.net/project/Spatial-Scaling-Challenge-COST-Action-CA17134-SENSECO-Working-Group-1</a></strong>.</p> <p> </p>
Fig. 1 in Lepisosteiform Fish (Holostei) Ganoid Scales From The Middle Jurassic Deposits Of Ukraine
Fig. 1. Location of the Pekari fossil site on the map of Ukraine, the portrait of Professor O. S. Rogovich with the original (above) and additional labels (below) of the described material.
Fig. 2 in Lepisosteiform Fish (Holostei) Ganoid Scales From The Middle Jurassic Deposits Of Ukraine
Fig. 2. Ganoid scales of Lepisosteiformes gen. et sp. indet. from Pekari: A1-A2 — No. 391/109; B1-B2 — No. 391/107a; C1-C2 — No. 391/107b; D1-D2 — No. 391/110; E1-E2 — No. 391/108; F1-F2 — No. 391/107d; G1- G2 — No. 391/107c. Outer view in A1, B1, C1, D1, E1, F1, G1, inner view in A2, B2, C2, D2, E2, F2, G2. Abbreviations: bp, basal plate; dap, dorsal anterior process; dp, dorsal peg; gl, ganoin layer; gr, ganoin ridges.
Fig. 3 in Sex Differences And Sex Identification In The Small-Scaled Scorpionfish, Scorpaena Porcus (Scorpaenidae, Scorpaeniformes)
Fig. 3. Coefficient of variation (CV, %) for relative values of plastic features in adult males (M) and females (F).
Fig. 2 in Sex Differences And Sex Identification In The Small-Scaled Scorpionfish, Scorpaena Porcus (Scorpaenidae, Scorpaeniformes)
Fig. 2. Coefficient of variation (CV, %) for 21 plastic characters in adult males (M) and females (F).
Fig. 1 in Sex Differences And Sex Identification In The Small-Scaled Scorpionfish, Scorpaena Porcus (Scorpaenidae, Scorpaeniformes)
Fig. 1. Differentiation of males of the small-scaled scorpionfish on absolute values of 21 plastic characters.
Supplementary Material of : Large-Scale 3D Image Segmentation Using Scattering Networks
<p>The reader will find here the supplementary material associated with the manuscript "Large-Scale 3D Image Segmentation Using<br> Scattering Networks" submitted to IEEE Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2022.</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.