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476 results for “footprints”
Figure 2 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 2. Monthly average air temperature, total and effective precipitation values in Van.
Figure 8 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 8. Distribution of WF , and WF by years in Van province.
Figure 1 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 1. The study area.
Figure 4 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 4. Distribution of WF in Van province.
Figure 9 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 9. Distribution of WF in Van province when feed crops total are included in WF .
Mapping shallow groundwater solute footprints in arid regions using a hydrologically enhanced species distribution model
<p>The topography-only SDM of shallow groundwater and deep groundwater, the final models-SDM maps of shallow groundwater, their improvements, the original dataset of water chemistry, and the related R script in the study are available here</p>
STILT footprints data set 2
<p>This repository contains the second batch of training data sets of measurement footprints.</p> <p>The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport".</p> <p>Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/.</p> <p>The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.</p>
Data for: Bivalve δ15N isoscapes provide a baseline for urban nitrogen footprint at the edge of a World Heritage coral reef
<p>This dataframe presents the d<sup>15</sup>N signature of 348 long-lived benthic bivalves from 12 species. Individuals were trapped at 27 sites in 2012 around the Peninsula of Nouméa, New Caledonia. For each bivalve specimen, muscle tissues were dissected and stored frozen. Muscles were freeze-dried, ground into powder, and weighed in tin cups for isotopic analysis (about 1mg in 4 × 6 mm tin cups). Muscle samples were analyzed using a Thermo Delta Advantage mass spectrometer in continuous flow mode connected to a Costech Elemental Analyzer via a ConFlo IV at Union College (Schenectady, NY, USA). Ammonium sulfate [IAEA-N-2], caffeine [IAEA-600], and an in-house acetanilide were used as standards; measurements of δ<sup>15</sup>N are reported to atmospheric nitrogen. The uncertainty for δ<sup>15</sup>N measurements was ±0.15‰ based on repeated analysis of an in-house acetanilide standard.</p> <p>The data base is made of 348 raws corresponding to specimens, and 5 columns presenting an ID, the collection site, the position within the laggon , the species and the measured D15N value. </p>
Model Fit Figures for: The 100 pc White Dwarf Sample in the SDSS Footprint II. A New Look at the Spectral Evolution of White Dwarfs
<p>Figures for the model atmosphere fits to the spectroscopically confirmed white dwarfs in the 100 pc sample and the SDSS footprint (<span>arXiv:2412.04611). </span></p>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
mining-deforestation-footprint-EU-results-sec
<p>Sector-level footprint results from https://github.com/SLuckeneder/mining-deforestation-footprint-EU. Can be downloaded and stored in the results folder instead of running the model.</p>
A "Dirty" Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils
<p>Amazonian rainforests once thought to hold an innate pristine wilderness, are increasingly known to have been densely inhabited by populations showing a diverse and complex cultural background prior to European arrival. To what extent these societies impacted their landscape is unclear. Amazonian Dark Earths (ADEs) are fertile soils found throughout the Amazon Basin, created by pre-Columbian societies as a result of more sedentary habits. Much is known of the chemistry of these soils, yet their zoology has been neglected. Hence, we characterised soil macroinvertebrate communities and activity in these soils at nine archaeological sites and adjacent reference soils in three Amazonian regions, totaling eighteen sampling sites. Furthermore, we characterized various soil chemical and physical properties associated with soil fertility.</p> <p>The current dataset contains data on soil macroinvertebrate biodiversity (26 taxa), with a special focus on termites, earthworms and ants. It also contains data on soil macromorphology, bulk density and porosity, soil carbon, nitrogen, macro and micronutrients, magnetic susceptibility and apparent electrical conductivity. The results show similar overall macrofauna morphospecies richness in ADE and adjacent soils, but distinct communities in each soil type. They also show higher soil fauna activity in ADEs when compared to adjacent reference soils, associated with greater earthworm populations. Finally, they also confirm the well-known high soil fertility in ADEs compared with adjacent soils. Land use was an important determinant of both macrofauna biodiversity and soil fertility. These findings support the idea that humans have built and sustained a contrasting high fertility ecosystem that persisted until our days, altering biodiversity distribution patterns in Amazonia.</p>
Water scarcity and water footprint estimates
<p>Intra-state water conflicts have increased substantially over the past few decades particularly in developing regions, and partly attributed to water scarcity. However, empirical studies linking water scarcity and violent conflicts are sparse, while existing quantitative studies have used mostly climate variables (precipitation and temperature) to understand this link. Most studies that have used climate variables concluded that they were not strong predictors of water conflicts. The aim of this study was to identify water scarcity hotspots and to understand the links between water scarcity and violent conflicts across the Sahel and Lake Chad Basin (LCB) over the period 2000-2021. To achieve this, we combine outputs from a global hydrological model and demographic data to develop six water scarcity metrics. The developed metrics show varying levels of water scarcity across the study region. The Falkenmark index across all capital cities (Ouagadougou-Burkina Faso, Ndjamena-Chad, Bamako-Mali, and Niamey-Niger), and Maradi-Niger and Jigawa & Kano states in Nigeria was less than 100 m<sup>3</sup>/capital/year, indicating acute water scarcity in those areas. Findings further indicated that green water scarcity (GWS) and the Falkenmark index were closely linked with water conflicts compared to the other metrics. Our findings suggest that water conflicts cannot be explained by hydroclimatic factors alone without incorporating other socioeconomic variables like demographic information. Results from this study may be used by stakeholders to tackle endemic water scarcity and to predict and mitigate water conflicts in the region.</p>
Data for 'Assessing the value of biodiversity-specific footprinting metrics linked to South American soy trade'
<p>Underlying data for publication 'Assessing the value of biodiversity-specific footprinting metrics linked to South American soy trade'. </p>
The Legacy Environmental Footprints of Manufactured Capital
<p>To enhance the clarity and coherence of the statement, you may consider revising it as follows:</p> <p>By utilizing the data and Matlab scripts made available here, one can produce all the visual components featured in the article entitled "The Legacy Environmental Footprint of Manufactured Capital." This comprehensive work is the result of collaborative research among various distinguished scholars and prestigious institutions, namely:</p> <p>Ranran Wang<sup>1*</sup>, Edgar G. Hertwich<sup>2*</sup>, Tomer Fishman<sup>1</sup>, Sebastiaan Deetman<sup>1</sup>, Paul Behrens<sup>1</sup>, Wei-qiang Chen<sup>3</sup>, Arjan de Koning<sup>1</sup>, Ming Xu<sup>4</sup>, Kira Matus<sup>5</sup>, Hauke Ward<sup>1</sup>, Arnold Tukker<sup>1</sup>, Julie B. Zimmerman<sup>6</sup></p> <p><sup>1</sup>Institute of Environmental Sciences (CML), Leiden University; Leiden, The Netherlands.</p> <p><sup>2</sup>Department of Energy and Process Engineering, Norwegian University of Science and Technology; Trondheim, Norway.</p> <p><sup>3</sup>Institute of Urban Environment, Chinese Academy of Sciences; Xiamen, China.</p> <p><sup>4</sup>School of Environment, Tsinghua University; Beijing, China.</p> <p><sup>5</sup>Division of Public Policy, Hongkong University of Science and Technology; Hong Kong, China.</p> <p><sup>6</sup>School of the Environment, Yale University; New Haven, United States.</p> <p>Here is the Abstract of the article:</p> <p>The foundations of today's societies are provided by manufactured capital accumulation driven by investment decisions through time. Reconceiving how the manufactured assets are harnessed in the production-consumption system is at the heart of the paradigm shifts necessary for long-term sustainability. Our research integrates 50 years of economic and environmental data to provide the global legacy environmental footprint (LEF) and unveil the historical materials extractions, greenhouse gas (GHG) emissions, and health impacts accrued in today's manufactured capital. We show that between 1995-2019, global LEF growth outpaced GDP and population growth, and the current high level of national capital stocks has been heavily relying on global supply chains in metals. The LEF shows a larger or growing gap between developed and less-developed economies while economic returns from global asset supply chains disproportionately flow to developed economies, resulting in a double burden for less-developed economies. Our results show ensuring best-practice in asset production while prioritizing wellbeing outcomes is essential in addressing global inequalities and protecting the environment. Achieving this requires a paradigm shift in sustainability science and policy, as well as in green finance decision-making, to move beyond the focus on the resource use and emissions of daily operations of the assets and instead take into account the long-term environmental footprints of capital accumulation.</p>
Rove beetle (Staphylinidae) assemblages following the cumulative effect of wildfire and linear footprint in Boreal treed peatlands of northeastern Alberta (Canada)
<p>Cumulative effects of anthropogenic and natural disturbances have become increasingly relevant in the context of biodiversity conservation. Oil and gas (OG) exploration and extraction activities have created thousands of kilometers of linear footprints in boreal ecosystems of Alberta, Canada. Among these disturbances, seismic lines (narrow corridors cut through the forest) are one of the most common footprints and have become a significant landscape feature influencing the maintenance of forest interior habitats and biodiversity. <a name="_Hlk118288505"></a>Wildfire is a common stand-replacing natural disturbance in the boreal forest, and as such, it is hypothesized that its effects can mitigate the linear footprint associated with OG exploration, but only a few studies have examined its effectiveness. We studied the short-term (1 year post-fire) response of rove beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire (Fort McMurray). While rove beetle species richness was higher in seismic lines in both burned and unburned habitats compared to the adjacent peatland, diversity was greater only in seismic lines of burned areas. Abundance was lower in the burned adjacent peatland but similarly higher in the remaining habitats. Assemblage composition on seismic lines was significantly different from that in the adjacent forest and edge habitats within both burned and unburned sites. Moreover, species composition in burned seismic lines was different to either unburned lines or burned forest and edge. <a name="_Hlk89787659"></a><em>Euaesthethus laeviusculus</em> and <em>Gabrius picipennis</em> were indicator species of burned line habitats, are sensitive to post-fire landscape and can occupy wet habitats with moss cover more efficiently than when these habitats are surrounded by unburned forest. Although these results are based on short-term responses, they suggest that wildfire did not reduce the linear footprint, and instead, the cumulative effect of these two disturbances had a more complex influence on rove beetle recovery at the landscape level than for other invertebrates. Therefore, continued monitoring of these sites can become useful to evaluate changes over time and to better understand longer-term biodiversity responses to the cumulative effects of wildfire and linear disturbances in boreal treed peatlands, given the long-lasting effect of such disturbances.</p>
The Metadata of "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications"
<p>This resource contains all the raw data associated with the article "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications." This raw data underpins the conclusions presented in the article.</p>
The Biodiversity Footprint Database
<p><strong>The Biodiversity Footprint Database</strong> contains global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. <strong>The data is currently non peer-reviewed and under submission. The database will be open access after publication.</strong> The preprint of the manuscript can be found from: <a href="https://doi.org/10.48550/arXiv.2309.14186">https://doi.org/10.48550/arXiv.2309.14186</a></p> <p><strong>About the units</strong></p> <p>The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.</p> <p>Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).</p> <p><strong>Content of files</strong></p> <p><em>BiodiversityFootprintDatabase.xlsx</em></p> <p>The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.</p> <p><em>BiodiversityFootprintDatabase_DetailedData.zip </em></p> <p>The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.</p> <p><em>20220406_Exio3stressorcode _2011.py </em>& <em>20220406_Exio3StressorAggregationCode_2011.py </em></p> <p>The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).</p>
Laetoli footprints
These footprints were made about 3.6 million years ago by a bipedal hominid, likely Australopithecus afarensis. They are the earliest direct evidence of homonid bipedalism, though it is likely Ardipithicus was walking upright two million years earlier. Disclaimer: This file is not made directly from scan data. I used available topographic imagery to create a rough approximation, and cleaned up the result to make an idealized artistic representation of the footprints. There is no scientific value to the file, and it is not a replica but an interpretation and close approximation to the original. Source: Objaverse 1.0 / Sketchfab
P. maxima footprints from N16 quadrate
**Description:**<br> **Location**: Site 1 of Ipolytarnóc locality, Hungary<br> **Position**: N16 quadrate of site 1<br> **Age**: Lower Miocene<br> **Material**: A trackway includes (from the bottom up) right manus (N16/1), right pes (N16/2), pes (N16/3) and right manus (N16/4)<br> **Reference**: Gábor Botfalvai, János Magyar, Veronika Watah, Imre Szarvas & Péter Szolyák (2022): Large-sized pentadactyl carnivore footprints from the early Miocene fossil track site at Ipolytarnóc (Hungary): 3D data presentation and ichnotaxonomical revision, Historical Biology,<br> DOI: 10.1080/08912963.2022.2109967<br> Source: Objaverse 1.0 / Sketchfab
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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.