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1,610 results for “economic”
FIGURES 39–50. 39–41. Gracilaria cornea. 39 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURES 39–50. 39–41. Gracilaria cornea. 39. Habit. Scale bar = 4 cm. 40. Medullary and cortical cells. Scale bar = 250 μm. 41. Tetrasporangia in cortex. Scale bar = 30 μm. 42–43. Gracilaria flabelliformis. 42. Habit. Scale bar = 1.5 cm. 43. Medullary and cortical cells. Scale bar = 260 μm. 44–45. Gracilaria mammillaris. 44. Habit. Scale bar = 2 cm. 45. Medullary and cortical cells, and a mature cystocarp. Scale bar = 200 μm. 46–48. Gracilaria microcarpa. 46. Habit. Scale bar = 3 cm. 47. Medullary and cortical cells. Scale bar = 130 μm. 48. Mature cystocarp. Scale bar = 60 μm. 49–50. Gracilaria tikvahiae. 49. Habit. Scale bar = 2 cm. 50. Medullary and cortical cells. Scale bar = 130 μm.
FIGURES 17–27. 17 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURES 17–27. 17. Laurencia intricata. Medullary and cortical cells. Scale bar = 30 μm. 18–19. Yuzurua poiteaui var. gemmifera. 18. Habit. Scale bar = 3cm. 19. Pericentral, medullary and cortical cells. Scale bar = 120 μm. 20–21. Hypnea musciformis. 20. Habit. Scale bar = 2 cm. 21. Medullary and cortical cells. Scale bar = 120 μm. 22–26. Eucheumatopsis isiformis. 22. Habit. Scale bar = 4 cm. 23. Medullary filaments and cortical cells. Scale bar = 460 μm. 24. Mature cystocarp. Scale bar = 110 μm. 25. Tetrasporangium laterally attached. Scale bar = 25 μm. 26. Habit with smooth surfaces. Scale bar = 10 cm. 27. Meristotheca cylindrica. Habit. Scale bar = 4 cm.
FIGURE 1 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURE 1. Map of the Yucatan Peninsula showing the sites where red algal drifts were collected. Black dots indicate the location of the sampling sites (GPS coordinates are indicated in Table 1). The numbers correspond to the names of the sites.
FIGURES 2–5 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURES 2–5. Algal drifts in the Yucatan Peninsula. 2–3. Campeche coasts with high biomass. 2. Scale bar = 50 cm. 3. Scale bar = 2 m. 5–6. Yucatan coasts with low biomass. 5. Scale bar = 1 m. 6. Scale bar = 30 cm.
FIGURES 28–38. 28–29. Meristotheca cylindrica. 28 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURES 28–38. 28–29. Meristotheca cylindrica. 28. Medullary filaments and cortical cells. Scale bar = 140 μm. 29. Immature cystocarp. Scale bar = 220 μm. 30–32. Tepoztequiella rhizoidea. 30. Habit. Scale bar = 3.5 cm. 31. Medullary filaments and cortical cells. Scale bar = 90 μm. 32. Tetrasporangia basally attached (arrowhead). Scale bar = 30 μm. 33–34. Gracilaria blodgettii. 33. Habit. Scale bar = 2 cm. 34. Medullary and cortical cells. Scale bar = 140 μm. 35–36. Gracilaria caudata. 35. Habit. Scale bar = 4 cm. 36. Medullary and cortical cells and a mature cystocarp. Scale bar = 310 μm. 37–38. Gracilaria cervicornis. 37. Habit. Scale bar = 3 cm. 38. Medullary and cortical cells. Scale bar = 150 μm.
FIGURES 6–16. 6 in Marine red algae (Rhodophyta) of economic use in the algal drifts from the Yucatan Peninsula, Mexico
FIGURES 6–16. 6. Jania pumila, main axes showing genicula, intergenicula and branching pattern. Scale bar = 5 mm. 7–8. Acanthophora spicifera. 7. Habit. Scale bar = 2 cm. 8. Medullary and cortical cells. Scale bar = 70 μm. 9–10. Alsidium seaforthii. 9. Habit. Scale bar = 2 cm. 10. Pericentral, medullary and cortical cells. Scale bar = 100 μm. 11–12. Alsidium triquetrum. 11. Habit. Scale bar = 3 cm. 12. Pericentral and medullary cells. Scale bar = 145 μm. 13–14. Digenea simplex. 13. Habit. Scale bar = 3.5 cm. 14. Pericentral and medullary cells. Scale bar = 110 μm. 15–16. Laurencia intricata. 15. Habit. Scale bar = 2 cm. 16. Superficial cherry bodies. Scale bar = 50 μm.
Optimizing Sustainable Dairy Farming: A Techno-Economic Analysis of Graphene Ranch Restoration
<p> </p> <p>This dataset, titled Standardized Dairy Farm Cost Output Table, contains financial and production information related to the establishment and operation of a proposed dairy farm. It includes initial investment costs such as stock cows, fixed assets, and working capital, as well as production capacity and budget projections over multiple years.</p> <p>Key sections include:<br>Initial Cost of Investment: Covers items like stock cows and fixed assets.<br>Production Capacity: Provides figures on dairy farm output over the years.<br>Budget Projections: Displays financial allocations and expected expenditures over different time periods.</p> <p>The dataset consists of multiple columns spanning projected years and various financial indicators to help estimate the cost-output relationship in dairy farming operations.</p>
Techno-economic comparison of power-to-Ammonia and biomass- to-Ammonia plants using electrolyzer, CO 2 capture and water-gas- shift membrane reactor
<p>A set of imput data used for the paper entitled: Techno-economic comparison of power-to-Ammonia and biomass-<br>to-Ammonia plants using electrolyzer, CO 2 capture and water-gas-shift membrane reactor </p>
China's Greener Production Reduces East-to-West Pollution Transfers and Alleviates Environmental-Economic Inequalities
<p>The data contains 1. Pro-andCon-base_APE_Value added (the production- and consumption-based APE and value added in 2007, 2012, 2017, and the change); 2. f_d (the production- and consumption-based emission intensity (<em>f</em>) and value added intensity (<em>d</em>) in 2007, 2012, 2017); 3. REI_REIC (the REI index in 2007 and 2017, and REIC index); 4. ape_flow2007 (the APE flows in 2007); 5. ape_flow2012 (the APE flows in 2012); 6. ape_flow2017 (the APE flows in 2017); 7. ape_flowchange (the change of the APE flows from 2007 to 2017); 8. va_flow2007 (the value added flows in 2007); 9. va_flow2012 (the value added flows in 2012); 10. va_flow2017 (the value added flows in 2017); 11. va_flowchange (the change of the value added flows from 2007 to 2017);</p>
Synthetic natural gas (SNG) production with higher carbon recovery from biomass: Techno-economic assessment
<p>Dataset for the paper: Synthetic natural gas (SNG) production with higher carbon recovery from biomass: Techno-economic assessment</p>
Impact of format on comprehension of economic evaluations. The FORM-EE Study.
<p><strong>Technical notes and documentation</strong></p> <p>This publication contains the databases used in the paper for FORM-EE project (general public and professionals), R analysis script for reproducibility purposes, questionnaires used and formats presented to the target audience (infograhic, text and video for the general public; policy brief and executive summary for professionals).</p> <p><strong>Aims of FORM-EE project:</strong></p> <p>Cost-effectiveness analyses of health technologies have become a part of the decision-making process in healthcare policies. Nevertheless, economic results are not always presented in comprehensible formats for non-technical audiences, such as the general population, healthcare professionals or decision-makers. The main objective of the FORM-EE Project was to measure the impact of the format on comprehension of the key messages of an economic evaluation by non-technical audiences. Besides, the perceived usefulness and acceptability of the formats were also explored. The design of the formats reflected the target audience: for the general public, plain language was used in infographic, text and video format, while for professionals from the healthcare sector, more specialized language was used in executive summary and policy brief formats. </p> <p>Files included in this publication:</p> <ul> <li>FORM-EE datos pobl gral.xlsx (general population dataset)</li> <li>FORM-EE datos profesionales.xlsx (professionals dataset)</li> <li>Cuestionario FORM-EE Poblacion gral.pdf (general population questionnaire)</li> <li>Cuestionario FORM-EE Profesionales.pdf (professionals questionnaire)</li> <li>FORM-EE_pg_infografia.pdf (infographic format)</li> <li>FORM-EE_pg_texto-sencillo.pdf (text format)</li> <li>FORM-EE_pg_video.mp4 (video format)</li> <li>FORM-EE_prof_policybrief.pdf (policy brief format)</li> <li>FORM-EE_prof_resumen-ejecutivo.pdf (executive summary format)</li> </ul> <p><strong>What's new (v2)</strong></p> <ul> <li>Script FORM-EE clean_v2.R (updated analysis script made in R language)</li> <li>Formats translation FORM-EE.pdf (english translations of the formats)</li> </ul>
Global and regional projections of the economic burden of Asthma: A value of statistical life approach
Open the record for dataset details and reuse information.
Socio-Economic Factors Influencing Biogas Technology Uptake among Rural Households in Kuresoi South Sub-County, Nakuru County
<p>Biogas technology presents an alternative sustainable energy source that offers an opportunity to transform energy security, environmental sustainability, and reduction in greenhouse gas emissions. The research study explores the socioeconomic factors affecting biogas technology uptake among rural households in Kuresoi South Sub-County, Nakuru County. This is a descriptive study design based on the use of both primary and secondary data sources. The data collection covered 155 respondents through the use of questionnaires, focus group discussions, key informant interviews and observations. Selection of the respondents was done by using systematic random sampling, while data analysis was done using descriptive statistics, chi-square tests, and cross-tabulation supported by SPSS version 26. Results indicated that despite high levels of awareness, the adoption of biogas technology was low, with firewood remaining the primary source of energy in 68% of the households. Fixed dome and tubular were the biogas digester types in use, since they are relatively cheaper and more durable; however, economic factors, mainly household income, were the main determinant of uptake. The chi-square results indicated that there was a significant relationship between household income and uptake of biogas, χ² = 9.531, p = 0.048, implying that the poorer a household is, the greater the financial barrier to the technology. Level of education, too had a say in energy adoption; education and energy choice had a strong association-since χ² = 12.814, p = 0.002-which depicted that more educated households were more likely to adopt the technology. The gender factor is insignificant in influencing energy choices, underlining a proof from the fact that χ² = 2.119, p = 0.346, where broader socio-economic factors played a much greater role in decisions. This study also revealed out that radio was the effective channel for knowledge sharing and information dissemination related to biogas technology. On the other hand, partial understanding of the technical aspects has acted as a big barrier to the better diffusion of this technology. In conclusion, income levels and education are two main factors affecting the uptake of biogas technology. Enhanced education, targeted financial support and better outreach strategies go toward increasing adoption rates and supporting transitions to sustainable energy in rural areas.</p>
Data from: Evaluation of candidate DNA barcoding loci for economically important timber species of the mahogany family (Meliaceae)
There has been considerable debate regarding locus choice for DNA barcoding land plants. This is partly attributable to a shortage of comparable data from proposed candidate loci on a common set of samples. In this study, we evaluated main candidate plastid regions (rpoC1, rpoB, accD) and additional plastid markers (psbB, psbN, psbT exons and the trnS-trnG spacer) as well as the nuclear ribosomal spacer region (ITS1-5.8S-ITS2) in a group of land plants belonging to the mahogany family, Meliaceae. Across these samples, only ITS showed high levels of resolvability. Interspecific sharing of sequences from individual plastid loci was common. The combination of multiple loci did not improve performance. DNA barcoding with ITS alone revealed cryptic species and proved useful in identifying species listed in Convention on International Trade of Endangered Species appendixes.
FIGURES 7–12. Aphanogmus inamicus 7 in Two new species of Aphanogmus (Hymenoptera: Ceraphronidae) of economic importance reared from Cybocephalus nipponicus (Coleoptera: Cybocephalidae)
FIGURES 7–12. Aphanogmus inamicus 7) female forewing, 8) female habitus, 9) female mesosoma, 10) male antenna, 11) female antenna, 12) male genitalia.
FIGURES 1–6. Aphanogmus albicoxae 1 in Two new species of Aphanogmus (Hymenoptera: Ceraphronidae) of economic importance reared from Cybocephalus nipponicus (Coleoptera: Cybocephalidae)
FIGURES 1–6. Aphanogmus albicoxae 1) female forewing, 2) female mesosoma 3) female habitus, 5) female antenna, 5) male genitalia, 6) male antenna.
Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks: Dataset 2
<p>Coastal communities rely on levees and seawalls as critical protection against sea-level rise; in the U.S. alone, $300 billion in shoreline armoring costs are forecast by 2100. But despite the local flood risk reduction benefits, these structures can exacerbate flooding and associated damages along other parts of the shoreline—particularly in coastal bays and estuaries, where nearly 500 million people globally are at risk from sea-level rise. The magnitude and spatial distribution of the economic impact of this dynamic, however, are poorly understood. Here we combine hydrodynamic and economic models to assess the extent of both local and regional flooding and damages expected from a range of shoreline protection and sea-level rise scenarios in San Francisco Bay, California. We find that protection of individual shoreline segments (5-75 km) can increase flooding in other areas by as much as 36 million cubic meters and damages by $723 million for a single flood event, and in some cases can even cause regional flood damages that exceed the local damages prevented from protection. We also demonstrate that strategic flooding of certain shoreline segments, such as those with gradually sloping baylands and space for water storage, can help alleviate flooding and damages along other stretches of the coastline. By matching the scale of the economic assessment to the scale of the threat, we reveal the previously uncounted costs associated with uncoordinated adaptation actions and demonstrate that a regional planning perspective is essential for reducing shared risk and wisely spending adaptation resources in coastal bays.</p> <p>This dataset is associated with a github repository https://github.com/rmgriffin/OLU-flood-externalities</p> <p>This dataset is part of a two record data repository associated with the paper "Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks." This is part two.</p>
Data and code for the shiny app of the paper Economic disparity among generations under Paris Agreement
<p>This datasets contains the code and data used to generate the website <a href="https://climate-change.shinyapps.io/generation_disparity/?_ga=2.163158204.1233661101.1626298966-1055362809.1623199649">https://climate-change.shinyapps.io/generation_disparity/</a> in the paper Economic disparity among generations under Paris Agreement.</p>
Data from: Evidence of economical territory selection in a cooperative carnivore
<p>As an outcome of natural selection, animals are likely adapted to select territories economically by maximizing benefits and minimizing costs of territory ownership. Theory and empirical precedent indicate that a primary benefit of many territories is exclusive access to food resources, and primary costs of defending and using space are associated with competition, travel, and mortality risk. A recently-developed mechanistic model for economical territory selection provided numerous empirically testable predictions. We tested these predictions using location data from gray wolves (<i>Canis lupus</i>) in Montana, USA. The dataset included here contains the territory size estimates for each collared wolf and the characteristics of territories.</p>
Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks: Dataset 1
<p>Coastal communities rely on levees and seawalls as critical protection against sea-level rise; in the U.S. alone, $300 billion in shoreline armoring costs are forecast by 2100. But despite the local flood risk reduction benefits, these structures can exacerbate flooding and associated damages along other parts of the shoreline—particularly in coastal bays and estuaries, where nearly 500 million people globally are at risk from sea-level rise. The magnitude and spatial distribution of the economic impact of this dynamic, however, are poorly understood. Here we combine hydrodynamic and economic models to assess the extent of both local and regional flooding and damages expected from a range of shoreline protection and sea-level rise scenarios in San Francisco Bay, California. We find that protection of individual shoreline segments (5-75 km) can increase flooding in other areas by as much as 36 million cubic meters and damages by $723 million for a single flood event, and in some cases can even cause regional flood damages that exceed the local damages prevented from protection. We also demonstrate that strategic flooding of certain shoreline segments, such as those with gradually sloping baylands and space for water storage, can help alleviate flooding and damages along other stretches of the coastline. By matching the scale of the economic assessment to the scale of the threat, we reveal the previously uncounted costs associated with uncoordinated adaptation actions and demonstrate that a regional planning perspective is essential for reducing shared risk and wisely spending adaptation resources in coastal bays.</p> <p>This dataset is associated with a github repository https://github.com/rmgriffin/OLU-flood-externalities</p> <p>This dataset is part of a two record data repository associated with the paper "Economic evaluation of sea-level rise adaptation strongly influenced by hydrodynamic feedbacks." This is part one.</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)
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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.
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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.
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.