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336 results for “expenditure”
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Nutritional table to estimate the availability of nutrients in households from the Mexican National Survey of Household Income and Expenditures (ENIGH) 2008-2020
<p>The database contains the amount of six nutrients (calories, proteins, vitamin A and C, iron, and zinc) per 100 grams/mililiters for each of the food categories used in the Mexican National Survey of Household Income and Expenditures 2008-2020.</p>
Healthcare Expenditure and Demographic Trends: A Comparative Analysis of Selected Countries (2000-2024)
<p><strong><em><span>This research journal investigates the interplay between healthcare expenditure, life expectancy, and demographic characteristics across selected countries from 2000 to 2024. Utilizing quantitative analysis, the study examines healthcare spending as a percentage of GDP, average life expectancy, and the age distribution of populations. For instance, the USA's healthcare expenditure is projected to reach 19.2% of GDP by 2024, with an average life expectancy of 81.9 years. In contrast, Bangladesh's healthcare expenditure is anticipated to be 7.0% of GDP, with a life expectancy of 70.0 years. The findings reveal critical insights regarding the effectiveness and sustainability of healthcare systems, emphasizing the need for policy interventions that prioritize healthcare funding, especially in aging populations where the percentage of individuals aged 65 and older is expected to rise significantly—projected at 16.0% for the USA and 7.0% for Bangladesh in 2024</span></em></strong><strong><span>.</span></strong><strong><span> </span></strong></p>
Lithuanian Household Energy Expenditure and Energy Poverty Data, 2019
<p>This dataset provides data about household energy expenditure and energy poverty in Lithuania. The dataset contains detailed data about 5031 households and is based on the Lithuanian Survey on Income and Living Conditions (2019) micro dataset provided by Statistics Lithuania. It includes additional data derived from original survey data and energy poverty calculation results at household level.</p> <p>Duomenų rinkinyje pateikiami duomenys apie namų ūkių energijos išlaidas ir energijos nepriteklių Lietuvoje 2019 metais. Duomenų rinkinys apima 5131 namų ūkį. Rinkinio pagrindas - Pajamų ir gyvenimo sąlygų statistinio tyrimo duomenys, skelbiami Lietuvos Statistikos departamento. Duomenų rinkinys apima ir papildomus duomenis gautus remiantis originalios apklausos duomenimis bei energijos nepritekliaus skaičiavimų rezultatus namų ūkio lygmenyje.</p>
Global Tax Expenditures Database (GTED)
<p>The GTED collects all publicly available data on tax expenditures (TEs) published by national governments worldwide from 1990 onwards, covering a total of 218 jurisdictions. Based on a step-by-step search process, 116 jurisdictions are currently classified as <em>Non-reporting Jurisdictions.</em> The remaining 102 ones do provide some type of TE data, which was gathered by the GTED team.</p> <p>Wherever available, the GTED gathers revenue forgone estimates and number of beneficiaries of individual TE provisions. It also gathers metadata including the definition of the TE provision, its legal basis and duration.</p> <p>Each record in the GTED is classified in four main categories: Tax Type, Policy Objective, Beneficiaries and Type of TE used. In some cases, second- or third-level categories have been introduced. For instance, <em>Fuel Tax</em> data is categorised at the third level within <em>Tax Type: Taxes on Good and Services Excise Taxes Fuel Tax</em>. If the information for a record is not available or unclear, the respective category is classified as <em>Not stated/unclear</em>.</p> <p>When governments do not publish provision-level data but rather some kind of aggregated information, the GTED gathers this aggregate data. Likewise, if governments report on specific areas of TE only (such as tax incentives for investments, or TEs on income taxes) the GTED presents data on these areas alone. The terms <em>TE reporting</em> or <em>TE report</em> are used broadly, and refer to a large variety of public documents, ranging from annual, comprehensive reports on TEs that are part of governmental budget documentation to individual documents issued by a public body and providing some aggregate information on some specific TE mechanisms. As a minimum requirement, reports must contain some kind of information on the actual use of TE provisions. For instance, a list of available tax deductions for investments, provided by a governmental investment promotion agency, would not be considered a TE report unless they provide revenue forgone estimates or any other data that would allow users of the GTED to obtain information about the actual use of the respective TEs.</p> <p>The GTED distinguishes <em>regular</em> and <em>irregular</em> reporting. A sequence of reports from 1995 to 2005 would not be considered regular reporting in the GTED, since the country had reported on a yearly basis, but not anymore. Likewise, <em>regular</em> is not necessarily related to annual reporting. Germany, for instance, publishes federal subsidy reports including TE data every two years since 1967. A total of 16 such reports have been issued since 1990, containing data on 29 budget years (until 2021). The GTED counts this as 31 years reported, because data is provided on a year-by-year basis and can be consulted and analysed as such.</p> <p>The data is processed in a consistent format seeking to increase the level of longitudinal and cross-country comparability. Whereas revenue forgone estimates are provided as reported by governments (in local currency units, current prices), the GTED also provides figures converted into US dollars as well as indicators providing the revenue forgone through TE provisions as shares both of <em>GDP</em> and <em>Tax Revenue</em> – to compute these two indicators, data from the <a href="https://www.wider.unu.edu/project/government-revenue-dataset">UNU-WIDER Government Revenue Dataset</a> is used as input. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments.</p> <p>Besides all the effort put into ensuring comparability, cross-country analysis of TE data needs to be done cautiously. The main issue, which is inherent to TE data, regards <em>benchmarking</em>. TEs are defined as departures from – usually country-specific – normal tax structures or benchmarks. On this note, the GTED uses the data published by official governmental institutions, sticking to their own definitions of benchmarks, without trying to complement official figures or challenge what different countries consider as the standard tax system or the benchmark.</p> <p>When it comes to the methodology used by governments to compute the fiscal cost of TE provisions, the vast majority of countries report on TEs based on the <em>revenue forgone approach</em> that estimates the amount by which taxpayers have their tax liabilities reduced as a result of a TE based on their actual current economic behaviour. Since the revenue forgone methodology is static, the potential interconnections between different TE provisions are not taken into account when computing the fiscal cost of TEs based on it. Hence, aggregating revenue forgone estimates of the individual provisions computed separately and without taking behavioural changes into account would not result in a figure that represents the total cost of all TEs.</p> <p>While providing users of the database with the opportunity to draw comparisons across countries or country groups, we want to be clear that any such comparison should be mindful of different levels of reporting, differences in national benchmark systems and methodological shortcomings of revenue forgone estimations.</p> <p>Country Income Groups and Regional Classifications are based on the latest World Bank classifications.</p>
Inappropriate Expenditure Dataset
<p>Please read the Code Book/</p> <p>Author: Tomohiro Hosoi</p> <p> </p> <p>1. About the Dataset</p> <p>This dataset contains information on the amount of inappropriate expenditure in the South African Public Sector. The Public Finance Management Act (PFMA) regulates National and Provincial organs, while the Municipal Finance Management Act (MFMA) does Local Government (Metropolitan Municipality, Local Municipality and District Municipality). The dataset aims to estimate how much an institution uses its budget inappropriately.</p> <p> </p> <p>2. Procedure</p> <ul> <li>The Auditor-General of South Africa releases information on inappropriate expenditures in their annual reports. The Auditor General classifies three categories; (i) unauthorised expenditure; (ii) irregular expenditure; and (iii) fruitless and wasteful expenditure.</li> <li>The author sums up (i) to (iii) and names it “inappropriate expenditure”.</li> <li>The author also collects the annual expenditure of each institution from the national budget and Statistics South Africa’s releases.</li> <li>Then, the author calculates the share of inappropriate expenditure compared with total expenditure. The author named it an “inappropriate expenditure ratio”.</li> <li>The data are shown in both PFMA and MFMA respectively.</li> </ul>
Journal subscription expenditure in the UK 2017-2019
<p>This dataset contains payments made by UK higher education institutions for access to academic journals from ten publishers from 2017-2019. The data was obtained by sending Freedom of Information requests to institutions through the website https://whatdotheyknow.com. The total expenditure with these ten publishers from 2017-2019 was over £353 million.</p>
Data Storage for Baylis and Boomhower (2022): Fire Characteristics, Expenditures, and Other Miscellaneous Datasets
<pre># Description Zenodo data storage for large, non-proprietary data used in "The Economic Incidence of Wildfire Suppression in the United States", by Patrick Baylis and Judson Boomhower. Main OpenICPSR repository (contains code and main README.txt): https://www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/144601 # Contents This storage mirrors the following offline directories used in the code. Each .tar file contains a directory of the same name. To replicate the existing code, users should decompress each directory into raw/, following the structure used in the code. (Note: as described in the main README, running most of the code requires access to proprietary data which is not included in this storage). ## Resulting directory structure To be consistent with the original source code, included the .tar files should be decompressed into the following directory structure within the directory designated by the RAW global in 01_Code/globals.R in the main reposistory. raw/calfire/ raw/census/county-tract/ raw/census/income/ raw/census/populated_places raw/gacc/ raw/geo/ raw/gpw/ raw/hpi/ raw/incidents/CalFire/ raw/incidents/FAMWEB/ raw/incidents/InteriorDepartment/ raw/incidents/FEMA/ raw/incidents/KCFAST/ raw/MTBS/ raw/nifc/ raw/preparedness-spending/doi/ raw/preparedness-spending/usfs/ raw/roads/ raw/USFS/ raw/whp/ raw/wui/</pre>
South Haven Lighthouse Daily Expenditures Dataset
<p>This dataset was created from the digitized log “Journal of daily expenditure of oils, wicks, and chimneys at the South Haven Michigan light station on Black River” housed within the <a href="http://luna.library.wmich.edu/luna/servlet/detail/WMUwmu~90~90~1246041~154474:Journal-of-daily-expenditure-of-oil?sort=title%2Calternative_title%2Ccreator%2Cdate">South Haven Michigan Lighthouse Log collection at Western Michigan University</a>.</p> <p>The lighthouse keeper log notes the daily expenditure journal for the South Pierhead Light, South Haven, Michigan, from September 1, 1888 through August 31, 1892. Entries were recorded by the keeper James S. Donahue. The log provides details on the amount of oil, wicks and chimneys used at the lighthouse and also taken for consumption within the keeper's home. The keeper noted the light's order of lens, kind of light, number of wicks in burner, and diameter of outer burner. There are also details on the weather of the day, what time the lighthouse was lit and extinguished, and the length of time it remained lit.</p>
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis
<p><strong><span>Background: </span></strong><a name="_Hlk170909708"></a><span>The study analyzes the impact of public health spending on malnutrition among Peruvians, using data from the National Household Survey, the Central Reserve Bank of Peru, the National Institute of Statistics and Informatics and the Ministry of Economy and Finance from 2010. -2020. Previous studies have revealed the existing relationship of health spending with the reduction of malnutrition</span><span>.</span></p> <p><strong><span>Methods:</span></strong><span> A quantitative approach is considered, with an explanatory type of research using panel data methodology considering the bidimensionality of the data, which allows quantifying this effect for the Peruvian case using the National Household Survey, data from the Central Reserve Bank of Peru, as well as information from the National Institute of Statistics and Informatics and the</span><strong><span> </span></strong><span>Transparency Portal of the Ministry of Economy and Finance in the period 2010-2020.</span><strong><span> </span></strong></p> <p><strong><span>Results: </span></strong><span>The results show that public expenditure on health has a negative relationship with malnutrition; the rural sector has a positive relationship with malnutrition given the limitations present for access to adequate food. Similarly, the unemployment rate shows a positive relationship with malnutrition, given that being unemployed leads to a higher cause of malnutrition in the population, and the gross domestic product has a negative relationship with malnutrition, given that greater economic growth produces an impact on reducing malnutrition, with the greatest impact being on the rural population and the gross domestic product. </span></p> <p><strong><span>Conclusions:</span></strong><span> In the analysis period 2010-2020 in Peru, based on the panel data analysis, the impact of public health expenditure on reducing malnutrition is observed in 10 departments, achieving a reduction in malnutrition; while in 14 departments, this indicator has not been reduced.</span></p>
A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)
<p>Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837</p>
DATASETS FOR: A keystone avian predator faces elevated energy expenditure in a warming Arctic
<p> Here, we provide two datasets from a study in which we used triaxial accelerometers (Axy 4, Technosmart, 3g) to collect detailed behavioral records from little auks (<em>Alle alle</em>) at Ukaleqarteq (UK), East Greenland (70°44′N, 21°35′W) and Hornsund (HS) (77°00′N, 15°33′E; Svalbard archipelago), during the chick rearing period. We used this data to compile time activity budgets, from which we estimated daily energy expenditure (DEE). Data spans five years (2017-2021) at UK and two years at HS (2020, 2021). We assessed whether variation in DEE was affected by variability in climate change-sensitive environmental variables that affect availability of the little auk’s resource base of cold water zooplankton, that is sea surface temperature (SST) and sea ice coverage (SIC). SIC was only used for UK, since there was no appreciable sea ice at HS, which experiences higher average SST than UK. We also obtained small ~0.2-0.5 ml blood samples from the brachial veins of focal individuals to measure contamination from a potent chemical contaminant, mercury (Hg). We assessed the hypothesis that DEE is forced upward by challenging foraging conditions, but may be limited at some point due to energetic thresholds. We also assessed whether Hg contamination levels modified patterns of energy expenditure.</p> <p> In addition, to further examine the relationship that emerged between DEE and SST, we compiled a dataset of 12 site-year observations of average DEE of breeding little auks using data from Gabrielsen et al. (1991) (n = 13), Grémillet et al. (2012) (n = 70) and the present study. This dataset spanned 35 years (1986-2021) and 3 sites (UK, HS, and Kongsfjorden, KF). KF is another breeding colony of little auks on Svalbard that experiences even warmer SST than HS.</p>
The cost of movement: assessing energy expenditure in a long-distant ectothermic migrant under climate change
<p>Functions to simulate monarch migration under set weather conditions. Data for repsirometry measurements and weather stations are also included in ZIP folders. Functions include working example of movement based on literature values for thresholds. Functions can be modified for other species as needed. Weather station data were collected from NOAA LCD stations. Alternative data sources include Wunderground Personal Weather Station datasets. However, Wunderground requires an API to access their data unless you have a PWS in their system. Connecting a PWS to wunderground provides you an API key for accessing data. </p>
Data Hospital Beds, Physicians, Nurses and Expenditure for 20 Latin American Countries from 1960 to 2022
<p>Long-term quantitative series for 20 Latin American countries, spanning from 1960 to 2020, on the number of hospital beds, physicians, nurses and healthcare expenditure.</p> <p>Matus-Lopez, M. and Fernández Pérez, P. 2023. "Transformations in Latin American Healthcare: A Retrospective Analysis of Hospital Beds, Medical Doctors, and Nurses from 1960 to 2022". <em>Journal of Evolutionary Studies in Business</em>.</p> <p>The information was extracted from official reports and cross-country databases. Official reports were available in digital format in the Institutional Repository for Information Sharing (IRIS) of Pan American Health Organization (PAHO). They were summary of four-year reports on Health Conditions in the Americas (PAHO 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002a), annual reports of Basic Indicators (PAHO 2002b, 2007, 2008, 2010, 2013), Health in South America (PAHO 2012) and Core Indicators (PAHO 2016). Databases were Open Data Portal of the Pan American Health Organization (PLISA) (PAHO 2023), Core Indicator Database provided directly by PAHO (PAHO 2022), Data Portal of National Health Workforce Accounts of the World Health Organization (NHWA) (WHO 2022), and the Global Health Expenditure Database of the World Health Organization (GHED) (WHO 2023).</p> <p>Serie 1. Hospital Beds per 1,000 inhabitants</p> <p>Serie 2. Physicians per 10,000 inhabitants</p> <p>Serie 3. Nurses per 10,000 inhabitants</p> <p>Serie 4. Government spending on health, per capita. Constant US dollars of 2020</p> <p>Cite as:</p> <p> </p> <p> </p>
Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry
<p>This repository contains code and data for the research project 'Estimation of activity induced energy expenditure using thigh-worn accelerometry and machine learning approaches'.</p> <ul> <li>The <strong>code </strong>subfolder contains Jupyter Notebooks and a Python file with helper functions. Further, the models subfolder contains the trained models.</li> <li>The <strong>data </strong>subfolder contains the raw accelerometer files (AX) as well as the raw data from the indirect calorimetry (CPET). Further, different processing files can be found here. The file log_master.csv contains the sociodemographic and timestamp data.</li> <li>The <strong>figures</strong> subfolder contains all relevant figures, which are created as part of running the Jupyter Notebooks. These figures are also part of the research publication.</li> </ul>
Journal subscription expenditure in the UK 2010-2019
<p>This dataset contains payments made by UK higher education institutions for access to academic journals from ten publishers from 2010-2019. The data was obtained by sending Freedom of Information (FOI) requests to institutions through the website What Do They Know. The requests, and all original source data, can be found at https://www.whatdotheyknow.com/user/stuart_lawson/requests.</p> <p>The total expenditure with these ten publishers from 2010-2019 was over £982 million. This includes some gaps in the data, so the true figure is almost certainly greater than £1 billion.</p> <p>The data was originally produced in three stages:</p> <p>- Data for 2010-14 was published at https://doi.org/10.6084/m9.figshare.1186832</p> <p>- Data for 2015-16 was published at https://doi.org/10.6084/m9.figshare.4542433</p> <p>- Data for 2017-19 was published at https://doi.org/10.5281/zenodo.3828461</p> <p>These three datasets contain direct links to the original FOI requests. The present dataset is a combination of these three datasets and contains no additional data.</p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
data for an article about public expenditure and time in office
<p>The dataset offers data for a scientific article about public expenditure and time in power for 42 developing countries.</p>
Kangaroo fathers modulate maternal control of offspring sex but not post-partum maternal expenditure
<p>When sons and daughters have different fitness costs and benefits, selection may favor deviations from an even offspring sex ratio. Most theories on sex ratio manipulation focus on maternal strategies and sex-biased maternal allocation. Recent studies report paternal influences on both offspring sex ratio and post-partum sex-biased maternal allocation. We used long-term data on marked kangaroos to investigate if and how paternal mass and skeletal size, both determinants of male reproductive success, influenced (a) offspring sex in interaction with maternal mass, and (b) post-partum sex-biased maternal allocation. When mothers were light, the probability of having a son increased with paternal mass. Heavy mothers showed the opposite trend. A similar result emerged when considering paternal size instead of mass. Post-partum maternal sex-specific allocation was independent of paternal mass or size. Studies of offspring sex manipulation or maternal allocation would benefit from an explicit consideration of paternal traits, as paternal and maternal effects can modulated each other.</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.