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45 results for “resource assessment”
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Dataset linking to the publication "An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020"
<p>This dataset links to the study “An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020”. This study is published in the journal “Environmental Research Letters” which can be found at <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abd81b">https://iopscience.iop.org/article/10.1088/1748-9326/abd81b</a>. The dataset contains two files, one csv file, and one shape file. The two files contain the same data to meet the different users' needs. The dataset contains variables for assessing national forest monitoring data sources i.e., RS and/or NFI. Separate indicators namely 'Use of RS', and 'Use of NFI' were used to analyze the two data sources (RS and NFI). The description of each variable for these two indicators contained in the dataset is given in the Table below.</p> <table> <caption><strong>The description of the variables in the datase</strong>t <strong>for country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Variables Name</strong></td> <td><strong>Description of the variables</strong></td> </tr> <tr> <td>Country</td> <td>Country</td> </tr> <tr> <td>ISO_A3_CODE</td> <td>ISO A3 Code for country</td> </tr> <tr> <td>ADM0_CODE</td> <td>ADMO Code for country</td> </tr> <tr> <td>CONTINENT</td> <td>Continent</td> </tr> <tr> <td>Region</td> <td>Region</td> </tr> <tr> <td>RSInd_05</td> <td>Use of remote sensing (RS) for forest area (change) monitoring 2005 Indicator</td> </tr> <tr> <td>RSSc_05</td> <td>Use of RS for forest area (change) monitoring 2005 Score</td> </tr> <tr> <td>RSInd _10</td> <td>Use of RS for forest area (change) monitoring 2010 Indicator</td> </tr> <tr> <td>RSSc _10</td> <td>Use of RS for forest area (change) monitoring 2010 Score</td> </tr> <tr> <td>RSInd_15</td> <td>Use of RS for forest area (change) monitoring 2015 Indicator</td> </tr> <tr> <td>RSSc _15</td> <td>Use of RS for forest area (change) monitoring 2015 Score</td> </tr> <tr> <td>RSInd_20</td> <td>Use of RS for forest area (change) monitoring 2020 Indicator</td> </tr> <tr> <td>RSSc _20</td> <td>Use of RS for forest area (change) monitoring 2020 Score</td> </tr> <tr> <td>DRS05_20</td> <td>Difference ‘use of RS’ 2005-2020</td> </tr> <tr> <td>NFIInd_05</td> <td>Use of national forest inventories (NFI) for forest monitoring 2005 Indicator</td> </tr> <tr> <td>NFISc_05</td> <td>Use of NFI for forest monitoring 2005 Score</td> </tr> <tr> <td>NFIInd _10</td> <td>Use of NFI for forest monitoring 2010 Indicator</td> </tr> <tr> <td>NFISc _10</td> <td>Use of NFI for forest monitoring 2010 Score</td> </tr> <tr> <td>NFIInd_15</td> <td>Use of NFI for forest monitoring 2015 Indicator</td> </tr> <tr> <td>NFISc _15</td> <td>Use of NFI for forest monitoring 2015 Score</td> </tr> <tr> <td>NFIInd_20</td> <td>Use of NFI for forest monitoring 2020 Indicator</td> </tr> <tr> <td>NFISc _20</td> <td>Use of NFI for forest monitoring 2020 Score</td> </tr> <tr> <td>DNFI05_20</td> <td>Difference ‘Use of NFI’ 2005-2020</td> </tr> </tbody> </table> <p>Indicators and Scores in the above Table for showing the use of RS and NFI data for forest monitoring in Figure 1 (1a, 1b, and 2a, 2b) are related in the following way.</p> <table> <caption><strong>The indicator values and scores of the country capacity assessment</strong></caption> <tbody> <tr> <td><strong>Indicator</strong></td> <td><strong>Score</strong></td> </tr> <tr> <td>Low</td> <td>0</td> </tr> <tr> <td>Limited</td> <td>1</td> </tr> <tr> <td>Intermediate</td> <td>2</td> </tr> <tr> <td>Good</td> <td>3</td> </tr> <tr> <td>Very Good</td> <td>4</td> </tr> </tbody> </table> <p>The capacity changes from 2005 to 2020 in Figure 1 (1c & 2c) are related in the following way.</p> <table> <caption><strong>The indicator values and levels for country capacity changes</strong></caption> <tbody> <tr> <td><strong>Capacity change values</strong></td> <td><strong>Capacity change levels</strong></td> </tr> <tr> <td>1,2,3,4</td> <td>Increase</td> </tr> <tr> <td>0</td> <td>No change</td> </tr> <tr> <td>-1,-2,-3,-4</td> <td>Decrease</td> </tr> </tbody> </table> <p> </p>
Data from: Butterflies are not a robust bioindicator for assessing pollinator communities, but floral resources offer a promising way forward
<p>Monitoring pollinators is crucial for the evaluation of biodiversity and potential pollination services. Yet, efficiently monitoring multiple taxa over large areas can be costly. An alternative approach is using simple species bioindicators that represent the entire pollinator community. One of the requirements of a good bioindicator is that it can be easily identified to lower taxonomic levels and be sensitive to changes in habitat. This is the case for butterflies, a taxon for which many countries have a country-wide long-term monitoring scheme. We tested whether butterfly diversity can be used to predict diversity of bees and hoverflies both spatially and temporally. We surveyed 42 transects of the Dutch Butterfly Monitoring Scheme in 2020, to record species richness and abundance of butterflies, bees and hoverflies. We also recorded flower area and richness in the pollinator transects. To test whether pollinators with similar functional traits are more closely correlated than the entire pollinator community, we categorized bee and butterfly species according to their diet breadth (polyphagous vs. non-polyphagous), nitrogen-affinity (nitrophobous vs. nitrophilous larval resources) and body size. We used the same methods to test for temporal correlations over seven years for one site in Spain. Butterfly richness was not spatially correlated with bee richness (Pearson's r = 0.13), nor were the two taxa temporally correlated (Pearson's r = 0.02). Interestingly, hoverfly richness was spatially correlated with butterfly richness (Pearson's r = 0.43) and with bee richness (Pearson's r = 0.36) in the Netherlands and, hence, hoverflies might be slightly more suitable as a bioindicator of pollinator diversity in this area. Abundance of all three taxa showed no significant inter-correlation, except for correlations between diet specialist bees and butterflies (Pearson's r = 0.39). Importantly, all three taxa were strongly correlated with flower richness, but they varied in their preferences for host plant families. This is in line with 75% of the plant-pollinator studies finding significant positive relations. For monitoring schemes to be effective in informing better pollinator conservation, they should expand to include bees and hoverflies as well as simple indicators of habitat quality such as floral resources.</p>
Byproduct-to-host ratios for assessing the accessibility of mineral resources
<p>This repository contains the supplementary information files of the article "Byproduct-to-host ratios for assessing the accessibility of mineral resources", published in the journal Environmental Sciences & Technology. This version of SI files is more documented than the previous one and with reference added to the article.</p> <ul> <li>"SI_1_BtH_ratios_v0.1.xlsx" contains both input data and results of the article</li> <li>"SI_2_Historic_prices.xlsx" contains the historical market price of mineral resources covered in the study</li> <li>"SI_3_Representavity_dataset.xlsx" containts the dataset required to evaluate the representativity of the dataset with regards to alternative estimates in the literature</li> <li>"SI_4_RR_LitReview.xlsx" show the data collected during the literature review of minerals recovery rates along global supply chains</li> <li>"SI_5_Production_2021.xlsx" provides the primary production of minerals in 2021</li> <li>"SI_6_Host_byproduct_Greffe2024.docx" provides additional information on the methodology and data collection</li> <li>"SI_7_Representativity_results.xlsx" contains the output results of the representativity check, using data from supporting information 1 and supporting information 3</li> </ul> <p>BtH ratios are obtained using "ResC" data in "SI_1_BtH_ratios_v0.1.xlsx" and using the byproduct_host_ratio python class available at: https://github.com/TitouanGreffe/BtH_ratios</p> <p>Article here: <a title="DOI URL" href="https://doi.org/10.1021/acs.est.4c05293">https://doi.org/10.1021/acs.est.4c05293</a></p>
The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
<p><strong>Background</strong></p> <p>The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resources. Hence, to protect insect biodiversity a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required.</p> <p><strong>New information</strong></p> <p>Because floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, where FloRes was derived from. Both databases contain the plant traits (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits are sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species <em>pluralis </em>(spp) and for 79 only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too less for a sufficient assessment of spatio-temporal availability of floral food resources.</p> <p>Because information of floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardizes the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 70 taxa.</p> <p>This is the first attempt to gather these eight traits from different literature sources in one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly gathered data to improve the quantification of food resources. Especially, the combination of pollen, nectar, and open flowers per square meter is, as far as we know, a novelty.</p> <p>The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, e.g., to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.</p>
Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment
<p>This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy.</p> <p>This repository contains:</p> <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>
The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
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Dataset for: Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes
<p>The dataset used to generate graphs for: Matthew Bradbury, Arshad Jhumka and Tim Watson. Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes. Proceedings of the Symposium on Applied Computing, ACM, 2021, 1-10.</p> <p>Also includes instructions for experiment setup.</p> <p>Scripts from https://github.com/MBradbury/iot-trust-task-alloc are required to analyse and graph these results.</p>
Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files
<p>The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven 'shock' events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a 'large ensemble' of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near the CINMS. The web application will be used by researchers from the University of California, Santa Barbara (UCSB) to analyze climate model output, and by CINMS staff to develop new indicators of shocks to marine ecosystems.</p>
Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa
<p><strong>Dataset Name:</strong><br><em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa.</em></p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li> <em>Resource Use Results:</em> BuildME Simulation Results</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa” https://doi.org/10.5281/zenodo.13380340.</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Support data for conference paper "Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions"
<p>Support data for article</p> <p>V. Kovtun, O. Kovtun, K. Grochla, and K. Połys, “Assessment of Communication Resource Allocation by the Transmission Control Protocol for the Target Virtual Connection under Competitive Conditions,” Electronics, vol. 13, no. 7. MDPI AG, p. 1180, Mar. 22, 2024. doi: 10.3390/electronics13071180.</p> <div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p> </div>
Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective - Supplementary material
<p>This is the additional material provided with the journal article "Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective" published in Renewable and Sustainable Energy Reviews (<a href="https://doi.org/10.1016/j.rser.2023.113699">https://doi.org/10.1016/j.rser.2023.113699</a>).</p> <p>Datasets are provided as NetCDF files for European maps and CSVfor Economically Attractive Resource curves.</p> <p>European and National plots are provided as PDF files.</p>
Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources
<p>Supporting Information from Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources</p>
Protecting the resource: an assessment of mitigation methods used to protect large trees from African elephant impact in a savanna system
<p>African elephants (<em>Loxodonta africana</em>) can alter the structural components of savanna ecosystems, often through the reduction of the large tree (>5 m height) cover component. Elephant impact can be amplified in small, protected areas, or areas where water is readily available to elephants. One management option is to protect large trees directly using applied mitigation methods to limit elephant impact. In this paper, we assessed and compared the effectiveness and logistical requirements of four mitigation methods that have been applied to protect large trees from elephant impact in South Africa's Greater Kruger National Park - namely African honeybees (<em>Apis mellifera scutellata</em>) in beehives; creosote oil in glass jars, concrete pyramids arranged in circles around trees, as well as wire-netting the trees' main stems. For each method, elephant impact levels and tree mortality rates were measured over a 2–5-year period depending on the method in use. Sample sizes ranged from 43 to 59 trees per mitigation method, with a comparable control, which was a tree of the same species and morphological dimensions but lacking any mitigation application. Beehives were the most effective method at reducing tree loss, significantly reducing tree mortality from 34% (6.8%/year) in control trees to only 10% (2%/year) over the five-year experimental period. However, beehives were the most expensive method to apply to a tree, although this cost can be compensated through honey sales. Concrete pyramids reduced tree loss when the combined pyramid radius was >1.5 m in length, whilst wire-netting was effective against bark-stripping by elephants but was still vulnerable to heavier forms of impact such as uprooting and stem snapping. Creosote jars did not prevent elephants from impacting treated trees. Our results provide managers with a toolkit for protecting large trees against elephant impact, commenting on both the efficacy and the logistical constraints for each method.</p>
Assessment of a Syringe Pump to Pre-eclamptic Women in a Low-resource Hospital
ClinicalTrials.gov study NCT02296931. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Retrospective Chart Review Study to Assess Characteristics, Treatment Outcomes and Resource Use of Adults Hospitalized for CAP and CSSTi Treated With Zinforo in Multiple Countries
ClinicalTrials.gov study NCT04198571. IPD Sharing: NO. Countries: 7. Publications: 1.
Protecting the resource: an assessment of mitigation methods used to protect large trees from African elephant impact in a savanna system
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Comparative Spatial Paleoecology: Assessing Niche Competition between Eocene North American Multituberculates and Rodents Regarding Forest Resources to Elucidate the Cause of Multituberculate Extinction
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Constructing visualization tools and training resources to assess climate impacts on the channel islands national marine sanctuary NetCDF files
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Ohio Department of Natural Resources, Division of Wildlife, Reservoir Productivity Assessment Water Chemistry 2006-2007
Limnological data provided represent information collected during 2006–2007 for a total of 153 reservoirs. Some reservoirs were sampled in a single year, others were sampled in both years. Nearly all reservoirs were sampled during July or August near the dam at the deepest part of the reservoir. Additional samples for particular systems were taken in other months and at other sites (i.e., near the inflow). Many samples have complete Secchi transparencies, suspended solids (both total and non-volatile suspended solids), total phosphorus, total nitrogen, and chlorophyll a concentrations. Others are missing various parameters; some samples are represented by only a Secchi transparency measurement. Morphometric data and landuse/land cover data are provided for those systems where it had been previously compiled. All 153 reservoirs are represented in the provided lake shapefile whereas only 117 were included in the watershed shapefile. ' Over a 2-year period (2006 and 2007) we sampled 109 reservoirs (all Ohio reservoiurs > 10 ha), located throughout Ohio, USA, whose watersheds contain a variety of land cover types and a wide range of eutrophication levels. All reservoirs were sampled at least once during July or August in 2006 or 2007. … a subset of 34 of these reservoirs were sampled once in both 2006 and 2007. In addition, 10 reservoirs (“reference reservoirs”) were sampled at least once per month during July and August of both 2006 and 2007. Lakes were sampled by Miami personnel and DOW field crews (Districu3, District 4, and District 5).’
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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.