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476 results for “footprints”
Xception trained model for classifying large ornithopod dinosaur footprints
<p>PLOS ONE: Classification of large ornithopod dinosaur footprints using Xception transfer learning</p><p>The trained model using Xception transfer learning, provided in https://github.com/CNUGeophysics/Xception_ornithopod.git</p>
Environmental Footprints of Greater Melbourne
<p>Environmental footprints of Greater Melbourne, 2021</p><p>Primary data sources: EXIOBASE MR EE SUT/IOT; ABS Australian National Accounts; ABS State Accounts; ABS Household Expenditure Survey (HES)</p><p>For more information on approach and methodology, contact <a href="mailto:research@opencorridor.org">research@opencorridor.org</a></p>
Data from : Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations
<p>This dataset contains representative 1D 1H NMR spectra and data used in the manuscript " Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations " . </p><p> </p><p>The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV and Azithromycin, AZI). </p><p> </p><p><strong>Supplementary File 1.zip = </strong>Representative 1D 1H NMR profiles of examined VE6 esometabolomes, </p><p> </p><p><strong>Supplementary File 2.xlsx</strong> = Average Mean ± Standard Deviations of NMR relative quantified data (integrals, a.u.) from examined VE6 esometabolomes. </p><p> </p><p><strong>Supplementary File 3.csv = </strong>p–values and associated False Discover Rate (FDR) derived from univariate ANOVA with Fischer's LDS post-hoc test comparisons carried out on NMR relative quantified data.</p><p> </p><p><strong>List of Supplementary Files derived from Metabolite Set Enrichment Analysis (MSEA) : </strong></p><p> </p><p><strong>Supplementary File 4.csv </strong>= Tabular Results from MSEA performed on VE6+ VE6- comparison.</p><p><strong>Supplementary File 5.csv </strong>= Tabular Results from MSEA performed on VE6+ RDV vs. VE6+ comparison.</p><p><strong>Supplementary File 6.csv </strong>= Tabular Results from MSEA performed on VE6+ AZI vs. VE6+ comparison.</p><p><strong>Supplementary File 7.csv</strong> = Tabular Results from MSEA performed on VE6+ R+A vs. VE6+ comparison.</p><p> </p>
Annual human footprint maps at 100-meter resolution from 1990 to 2020 for Qinghai-Tibet Plateau
<p>This Human footprint product is the first annual dataset with a resolution of 100 meters from 1990 to 2020 covering the Qinghai-Tibet Plateau (QTP). It integrates eight variables: population density, cropland, built environments, grazing activities, nighttime lights, roads, railways, and hydroelectric projects, each providing insights into various human impacts on the plateau. Additionally, the dataset's accuracy was assessed using 1,043 samples with a median resolution of 0.5 m, uniformly distributed across the QTP. These HF maps can serve as a critical tool for understanding the extensive human influence on the QTP, and can aid in conservation planning, resource management, and informing decision-making processes related to ecological restoration objectives. </p>
Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity
<p>Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (<em>Engystomops pustulosu</em>s), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales. </p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
Data for - EU's bioethanol potential from wheat straw and maize stover and the environmental footprint of residue-based bioethanol
<p>To reduce greenhouse gas (GHG) emissions, the European Union (EU) has targets for utilizing energy from renewable sources. By 2030, a minimum of 3.5% of energy in the EU’s transport sector should come from renewable biological sources, such as crop residues. This paper analyzed EU’s “advanced bioethanol” potential from wheat straw and maize stover and evaluated its environmental (land, water, and carbon) footprint. We differentiated between gross and net bioethanol output, the latter by subtracting the energy inputs in production. Results suggest that the annual amount of the sustainably harvestable wheat straw and maize stover is 81.9 Megatonnes (Mt) at field moisture weight (65.3 Mt as dry weight), yielding 470 PJ as gross (404 PJ as net) advanced bioethanol output. Calculated net advanced bioethanol can replace 2.95% of EU transport sector’s energy consumption. EU’s advanced bioethanol has a land footprint of 0.28 m<sup>2</sup> MJ<sup>−1</sup> for wheat straw and 0.18 m<sup>2</sup> MJ<sup>−1</sup> for maize stover. The average water footprint of advanced bioethanol is 173 L MJ<sup>−1</sup> for wheat straw and 113 L MJ<sup>−1</sup> for maize stover. The average carbon footprint per unit of advanced bioethanol is 19.4 and 19.6 g CO<sub>2</sub>eq MJ<sup>−1</sup> for wheat straw and maize stover, respectively. Using advanced bioethanol can lead to emission savings, but EU’s advanced bioethanol production potential is insufficient to achieve EU’s target of a minimum share of 3.5% of advanced biofuels in the transport sector by 2030, and the associated water and land footprints are not smaller than footprints of conventional bioethanol.</p>
The Carbon Footprint of Astronomical Research Infrastructures
<p><strong>Content of the directory</strong></p> <p>This record contains code and data that were used for the paper</p> <p><strong>Knoedlseder, J., et al. Estimate of the carbon footprint of astronomical research infrastructures, Nature Astronomy, in press</strong>.</p> <p><strong>Data</strong></p> <p>The file <em>ri-carbon-footprint.xls</em> contains all data that were used for the analysis in the paper.</p> <p>The file is an excel file containing the following tabs:</p> <ul> <li>Description - a description of the excel file</li> <li>Summary - summary of the findings of the carbon footprint estimates</li> <li>Carbon footprint (ground) - Master table for carbon footprint of ground-based observatories</li> <li>Carbon footprint (space) - Master table for carbon footprint of space missions</li> <li>Emission factors - Collection of emission factors used as input to the study</li> <li>Active infrastructures - List of astronomical facilities that were active worldwide in 2019</li> <li>Community - Astronomical community and IAU members for several countries (Ahn, S.H., Economic Power, Population, and Size of Astronomical Community, JKAS, 52, 159 (2019).</li> </ul> <p><strong>Code</strong></p> <p>The record contains three Python scripts.</p> <p><strong><em>adsquery.py</em></strong></p> <p>Script to query the ADS database to extract number of publications for a given facility. The script also determines the number of unique authors. It returns global numbers since the start of the mission or observatory operations, and numbers restricted to IRAP.</p> <p><em><strong>bootstrap.py</strong></em></p> <p>Script to bootstrap the considered facilities to extrapolate the carbon footprint to all infrastructures that exist worldwide. The script also produces Figure 1 of the paper.</p> <p><strong><em>carbonintensity.py</em></strong></p> <p>Script to generate various figures from the excel data, and in particular the carbon intensity Figure 2 of the paper. Running the script requires the "xlrd" Python module that can be installed via conda.</p>
Gaussian plume footprints
<p>Supplementary Gaussian plume footprints for the paper "Recovery of sparse urban greenhouse gas emissions". Code used with this dataset is found at <a href="https://doi.org/10.5281/zenodo.5900738">https://doi.org/10.5281/zenodo.5900738</a>.</p> <p>.</p>
Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.
<p>Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.:</p> <p><br> - Trait-based distances calculated for each pair of taxa;</p> <p>- CWM, CWM(LN) values, and their difference (CWMDIFF)</p> <p>Refer to the published articles for further details.</p>
2d adaptive extinction maps for the VVV footprint
<p><strong>2d adaptive resolution extinction maps for the VVV footprint</strong></p> <p>Extinction maps associated with the publication <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220510378S">Sanders et al. (2022, MNRAS)</a> </p> <p>Three maps are provided: E(J-Ks), E(H-Ks) and E(H-[4.5]). The E(J-Ks) and E(H-Ks) maps have been computed over the entire VVV footprint whilst the E(H-[4.5]) map is only computed for the inner -1.5<l<1.5, -1.5<b<1.5. To convert to AKs multiply by 0.449, 1.293, 0.700 respectively (or otherwise for other extinction laws).</p> <p>The files *.csv.gz give the colour excess (e*) and its spread (sigma_e*) at a set of Healpix labelled by their unique index. A series of Healpix resolutions have been used to provide higher resolution where needed (except for the E(H-[4.5]) map that is only at level=13). The indices are using the nested scheme given the Galactic coordinates (l,b). In this way, it is simple to handle the varying resolution (see <a href="https://ivoa.net/documents/MOC/">https://ivoa.net/documents/MOC/</a>).</p> <p>The colour excesses have been found from Gaussian fits to the red clump colours for (J-Ks) and (H-Ks) and an average over all giant stars for (H-[4.5]) (accounting for the weak gradient of the giant branch in the (H-[4.5]) vs. Ks colour-magnitude space). The spreads in extinction come from the width of the Gaussian peak for (J-Ks) and (H-Ks) and the width of the full distribution for (H-[4.5]) after subtracting the average photometric uncertainties and accounting for a (0.05,0.02,0.00) intrinsic colour width for (J-Ks, H-Ks, H-[4.5]) respectively.</p> <p>The provided file extinction_maps.py provides a class for reading in all extinction maps (version=JK,HK,H45 allows one to pick the required map) and querying the colour excess and its spread for large numbers of Galactic coordinates. Also there is functionality for finding the resolution of the map at a given location.</p> <p>The queries will throw a warning but return a value if the coordinate is outside the reliable footprint (the entire VVV footprint for JK and HK and the inner -1.5<l<1.5, -1.5<b<1.5 for H45).</p> <p>The example.ipynb notebook shows an example of querying the extinction map and plotting the result.</p> <p> </p> <p> </p>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Fig. 9 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 9: Histogram of the Mantel test assessing the relationship between genetic and morphologic distance for Gobius niger. Sim: simulations; Frequency: frequency values of the correlation between the genetic and morphologic distances. The dot represents the original value of the correlation between the distance matrices.
Fig. 6 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 6: PCA of the morphological variables of Gobius niger (standard length, SL; body height, BH; head length, HL; snout length, SnL; eye diameter, ED; first dorsal fin, DF1; second dorsal fin, DF2; anal fin, AF; pectoral fin, PF; ventral fin, VF) with projection of phenotypic groups. PC1 vs. PC2 and PC2 vs. PC3. The percentage of variation explained by each PC axis is given within parentheses.
Fig. 3 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 3: Cluster analysis associated with the similarity profile test (SIMPROF), based on abundances of Gobius niger, reveals reciprocal relations among the 20 sampled stations in the Marchica Lagoon using the Bray–Curtis distance.
Fig. 2 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 2: Picture of Gobius niger from the Marchica Lagoon showing the main measurements taken: total length (TL), standard length (SL), head length (LT), snout length (SnL), body height (BH), and eye diameter (ED).
Fig. 7 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 7: Linear regression of the principal component score axis (PC1) from morphometric measurements on the log standard length of Gobius niger with projection of phenotypic groups.
Fig. 8 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 8: Haplotype network constructed from 16S rDNA sequences of Gobius niger. The size of a particular circle reflects the haplotype frequency. The numbers indicate the nodes.
Fig. 1 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 1: Map showing the geographical localization of the Marchica Lagoon and the sampling stations of Gobius niger.
Fig. 4 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 4: Two-dimensional redundancy analysis (RDA) ordination representing the spatial distribution of Gobius niger related to the predictor variables selected through the best linear models based on distance (DISTLM). SM: suspended matter.
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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.
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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.
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