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4,356 results for “RED”
The door 1 at the red church in brno
The door 1 at the red church in brno. A good reference or gameobject to a computer game. Photogrammetry from 330 photos. Source: Objaverse 1.0 / Sketchfab
Aztec Black on Red Rim Sherd
Aztec black on red rim sherd from the 1559 Luna Settlement, Pensacola Florida To learn more about the Luna Settlement, visit www.uwf.edu/luna Model by Emma Dietrich This model was made using RealityCapture software by Capturing Reality. Source: Objaverse 1.0 / Sketchfab
Red Painted Pottery
This is currently housed at the Rollins College Archaeology Lab at Rollins College. Model made by Ellie Minette. Source: Objaverse 1.0 / Sketchfab
Aryballos with red figures, Camarina, Sicily
Aryballos with red figures. From the site of Camarina, Ragusa, Sicily, Italy. Classical Age. 5th century BC. In the collection of the Museo Archeologico Ibleo di Ragusa. Processed in Reality Capture from 725 images. GDH ID and Catalog No. 8838 Citation: We want to publicly acknowledge the collaboration of the ""Soprintendenza per i Beni Culturali ed Ambientali di Ragusa"" and the ""Parco Archeologico di Kamarina e Cava d'Ispica"". Our most sincere thanks to General CC, retired, Renato Scuzzarello, the archaeologist Dr. Saverio Scerra, the architect Giorgio Battaglia, the restorer Stefania Patti Occhipinti and the archaeologists Veronica Falcone and Alessandra D'Izzia, the custodians of Parco Archeologico Marco Giardina, Giovanna Brugaletta e Rosario Licitra" Source: Objaverse 1.0 / Sketchfab
03 BLUE HEAD RED BODY(青・赤)
小樽市松ヶ枝町 📍 [小樽市, 北海道](https://scaniver.se/L43.17673,140.98959) 北海道小樽市の消火栓は、独自の対応で3色で5パターンに塗り分けています。 Source: Objaverse 1.0 / Sketchfab
Chinese Red Door.
Modeled in Maya and textured in Substance Painter. Chinese Door with a logo made by myself… It can be downloaded,if you like it… More models are coming to Sketchfab.Stay tuned… #door #Sketchfab #Maya #3D #Begginer #red #color #china Source: Objaverse 1.0 / Sketchfab
16S rRNA sequences from Siganidae (S. rivulatus and S. luridus) gut microbiome in their native (Red Sea) and invaded (Mediterranean Sea) ranges
<p><span><span><span>T</span><span>he microbiome </span><span>of i</span><span>nvasive species </span><span>is increasing</span><span>ly</span><span> seen as</span><span> </span><span>a potential</span><span> </span><span>key factor of </span><span>their ecological</span><span> </span><span>success, </span><span>and t</span><span>his </span><span>appears</span><span> particularly true in herbivorous </span><span>invaders</span><span> whose digestive abilities rely on the microb</span><span>es</span><span> hosted in their </span><span>gut</span><span>. </span><span>We</span><span> characterize</span><span>d</span><span> the</span><span> gut microbiome of two invasive herbivorous fishes </span><span>(</span><span><em>S</em></span><span><em>iganus</em></span><span><em> rivulatus </em></span><span>and </span><span><em>S</em></span><span><em>iganus</em></span><span><em> luridus</em></span><span>) </span><span>in their </span><span>native (Red Sea) and invaded (Levantine Sea and Northern Crete) range</span><span>s. </span><span>We </span><span>found</span> <span>that </span><span>gut bacterial communities </span><span>contain a higher taxonomic and phylogenetic diversity </span><span>while</span> <span>bec</span><span>o</span><span>m</span><span>ing</span><span> increasingly different </span><span>from the native microbiome </span><span>as the fishes move away from the native zone. </span><span>This </span><span>shift </span><span>resulted in </span><span>the </span><span>homogenization of the microbiome</span><span>s</span><span> between </span><span>individuals from the same species </span><span>as well as between the two </span><span>species. Firmicutes and Tenericutes reduced drastically in abundance </span><span>while </span><span>Proteobacteria and Bacteroidetes </span><span>became more dominant in both species</span><span>. </span><span>This led to a modification of the functional potential of the gut microbiome associated with the metabolism of short-chanin fatty acids that also became more homogeneous in the invaded range. </span><span>Altogether, our results suggest that the plasticity of the gut microbiome in Siganidae could be a key factor underlying their ecological success </span><span>in </span><span>Mediterranean ecosystems</span><span>.</span></span></span></p>
The network characteristics of classic red tourist attractions in Shaanxi province, China
<p>Tourism flow is a significant tourism phenomenon and a hot topic of tourism geography research. This study, based on the perspective of combining <span>'virtual' and 'reality'</span><span>,</span> takes 13 classic red tourism scenic areas in Shaanxi <span>p</span>rovince as examples. It constructs a multi-source data network attention evaluation index and adopts social network analysis method to explore the network attention and tourism flow of the study case, and further investigates the relationship between the two. Th<span>e</span> study shows that: (1) The case sites have formed a spatial layout <span>of the </span><span>'</span>dense in the north and sparse in the south'. Among them, the total number of attractions in <span>n</span>orthern Shaanxi is the largest and most are concentrated in Yan<span>'</span>an; the total number of attractions in <span>s</span>outhern Shaanxi is the smallest and mo<span>st</span> scattered. (2) The overall network attention of the case sites is low, and there is variability in network attention of different types of tourist attractions, among which network attention of the attractions in Yan<span>'</span>an City is high. (3) The network structure of tourism flow in the case <span>has</span> the spatial characteristics of low density, <span>'</span>one level and multicore<span>'</span> and significant small network groups. (4) There are correlations and differences between network attention and tourism flow in the sites <span>in question</span>. Based on the differences between them, the attractions are classified into four types: high-high, high-low, low-high and low-low. In response to the above findings, this study proposes the principle of <span>'</span>precision identification and classification<span>'</span>, and proposes targeted development strategies such as <span>c</span>reating high-quality regional tourist routes<span>,</span> promoting the digital development of <span>tourist attractions</span>, and innovating <span>the </span>ways to promote attractions.</p>
Human health impact based on adult European consumers' dietary exposure to chemical contaminants and consumption of unprocessed red meat, processed meat, and legumes
<p>This dataset was generated within the PRISMA project (Marie Skłodowska-Curie Grant Agreement No. 101110615) for assessing the health impact for adult consumers from 25 European countries due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>The risk coefficients used to generate data are presented in Table S1.</p><p>File S2 presents the health impact based on the chronic dietary exposure to chemical contaminants for each country.</p><p>File S3 presents figures regarding the health impact for each country for adult consumers due to dietary exposure to chemical contaminants and consumption of food based on different dietary patterns.</p><p>File S4 presents the health impact based on the consumption of unprocessed red meat, processed meat, and legumes for each country.</p><p> </p>
Data from: Far red light increases maize volatile emissions in response to volatile cues from neighboring plants
<p>Plants perceive the presence and defense status of their neighbors through light and volatile cues, but how plants integrate both stimuli is poorly understood. We investigated if and how low Red to Far red light (R:FR) ratios, indicative of shading or canopy closure, affect maize (<em>Zea mays</em>) responses to herbivore-induced plant volatiles (HIPVs), including the green leaf volatile (<em>Z</em>)-3-hexenyl acetate. We modulated light signaling and perception by using FR supplementation and a <em>phyB1phyB2</em> mutant, and we determined volatile release as a response readout. To gain mechanistic insights, we examined expression of volatile biosynthesis genes, hormone accumulation, and photosynthesis. Exposure to a full blend of HIPVs or (<em>Z</em>)-3-hexenyl acetate induced maize volatile release. Short-term FR supplementation increased this response. In contrast, prolonged FR supplementation or constitutive phytochrome B inactivation in <em>phyB1phyB2 </em>plants showed the opposite response. Short-term FR supplementation enhanced photosynthesis and stomatal conductance and (<em>Z</em>)-3-hexenyl acetate-induced JA-Ile levels. We conclude that a FR-enriched light environment can prompt maize plants to respond more strongly to HIPVs emitted by neighbors, which might be explained by changes in photosynthetic processes and phytochrome B signaling. Our findings reveal interactive responses to light and volatile cues with potentially important consequences for plant-plant and plant-herbivore interactions.</p>
Fig. 9 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Fig. 9. Known distribution of Christopherus mictlantecuhtli gen. et sp. nov.
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Data from: Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape - patterns of genetic diversity, population structure and gene flow
<p><span><span>Red deer (</span><span><em>Cervus elaphus</em></span><span>) throughout central Europe are</span> impacted by different anthropogenic activities including habitat fragmentation, selective hunting, and translocations<span>. This has substantial influences on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess the genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. </span></span><span><span>We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, and anthropogenic infrastructures) has led to comparably low levels of genetic diversity. The mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. </span></span><span><span>Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. </span></span></p> <p><span><span>Based on these findings, we suggest that connectivity among existing management units needs to be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.</span></span></p>
Data from: Effects of resource availability and interspecific interactions on Arctic and red foxes' winter use of ungulate carrion in the Fennoscandian low-Arctic tundra
<p>In the Arctic tundra, predators face recurrent periods of food scarcity and often turn to ungulate carcasses as an alternative food source. As important and localized resource patches, carrion promotes co-occurrence of different individuals, and its use by predators is likely to be affected by interspecific competition. We studied how interspecific competition and resource availability impact winter use of carrion by Arctic and red foxes in low Arctic Fennoscandia. We predicted that presence of red foxes limits Arctic foxes' use of carrion, and that competition depends on the availability of other resources. We monitored Arctic and red fox presence at supplied carrion using camera traps. From 2006 to 2021, between 16 and 20 cameras were active for two months in late winter (288 camera-winters). Using a multi-species dynamic occupancy model at a week-to-week scale, we evaluated use of carrion by foxes while accounting for the presence of competitors, rodent availability and supplemental feeding provided to Arctic foxes. Competition affected carrion use by increasing both species' probability to leave occupied carcasses sites between consecutive weeks. This increase was similar for the two species, suggesting symmetrical avoidance. Increased rodent abundance was associated with a higher probability of colonizing carrion sites for both species. For Arctic foxes, however, this increase was only observed at carcasses unoccupied by red foxes, showing greater avoidance when alternative preys are available. Supplementary feeding increased Arctic foxes' carrion use, regardless of red fox presence. Contrary to expectations, we did not find strong signs of asymmetric competition for carrion in winter, which suggests that interactions for resources at a short time scale are not necessarily aligned with interactions at the scale of the population. In addition, we found that competition for carcasses depends on the availability of other resources, suggesting that interactions between predators depend on the ecological context.</p>
Karim et al; Main Fig: 8-B IF Magic Red and PFF-488-Green
Open the record for dataset details and reuse information.
Karim et al; Main Fig: 6-B-C IF EEA1-Red and PFF-488-Green
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Long-term Continuous Red and Near-infrared Channel Reflectance from MODIS, 2001-2023 (LCREF-MODIS)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark.</p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension:</strong> to include observations from the year of 2023.</li> <li><strong>Snow mask: </strong>we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. </li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-AVHRR v3.2 (1982-2023) is available at <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p> <p> </p>
Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel. </p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Pelt biting as a practical indicator of social and environment stress in farmed red deer
<p>Data sets used in the analyses of paper title "Pelt biting as a practical indicator of social and environment stress in farmed red deer"</p>
African Red Slip Ware Digital (ARS3D) - Ontology
<p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p> <p>The developed ontology uses CIDOC CRM and various extensions. The data mainly consists of objects (E24), on which e.g. applications (features, E25) can be found. These objects can be described semantically, e.g. by the shape or time epoch. Features are described with observations, e.g. a human type man standing and wearing a beard. These observations lead to interpretations whose arguments can be observations.</p>
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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.