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199 results for “quantities”
Groundwater and surface water quantity and quality measurement results for groundwater dependent ecosystem in Kazu leja, Latvia in 2019 and 2020
<p>Groundwater and surface water quantity and quality measurement results and methodology of the investigations of the groundwater dependant ecosystem in Kazu leja, Latvia in 2019 and 2020 is presented. The data comprises:</p> <ol> <li>Methodology</li> <li>Measurement site data [2_observation_sites.csv]</li> <li>Field and laboratory measurement results [3_field_and_lab_results.csv]</li> <li>Measurement uncertainty [4_measurement_uncertainity.csv]</li> <li>Automated water level, temperature, and electrical conductivity measurements [5_level_temperature_electricConductivity_loggers.csv]</li> </ol>
Data and Code: Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development
<h1><strong>Note</strong></h1> <p>Here we provide preprocessed data and analysis code used in "Cortical representations of symbolic and non-symbolic quantity expand but become estranged with learning and development".</p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu/">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual preprocessed volumes, normalized into the MNI template, and averaged across five TRs for each block (see paper for details about the preprocessing pipeline).</p> <p>The analysis code requires Python version 3.8.8, Nilearn version 0.8.1, and Scikit-learn version 0.24.1.</p> <p>If you have any questions, please send an email to nakai.tomoya [at] neuro.mimoza.jp. </p> <p> </p> <h1><strong>Usage</strong></h1> <pre>import PredysDecoding5ans_SearchLight_LOOCV as pdsl5 import PredysDecoding8ans_SearchLight_LOOCV as pdsl8 import PredysDecoding5to8_SearchLight as pdsl58 import PredysDecoding8to5_SearchLight as pdsl85 </pre> <h3>Within-format decoding for 5-year-olds (Figures 2A, B):</h3> <pre>pdsl5.IntraModalDec(TaskName='Dots') pdsl5.SaveNifti_PermTest(TaskName='Dots') pdsl5.IntraModalDec(TaskName='Digits') pdsl5.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding for 8-year-olds (Figure 2C, D):</h3> <pre>pdsl8.IntraModalDec(TaskName='Dots') pdsl8.SaveNifti_PermTest(TaskName='Dots') pdsl8.IntraModalDec(TaskName='Digits') pdsl8.SaveNifti_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding, paired tests between 5- and 8-year-olds (Figures 3A, B):</h3> <pre>pdsl5.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl5.SaveNifti_Paired_PermTest(TaskName='Digits') pdsl8.SaveNifti_Paired_PermTest(TaskName='Dots') pdsl8.SaveNifti_Paired_PermTest(TaskName='Digits')</pre> <h3>Within-format decoding across 5- and 8-year-olds (Figures 3C, D):</h3> <pre>pdsl85.IntraModalDec(TaskName='Dots') pdsl85.SaveNifti_PermTest(TaskName='Dots') pdsl85.IntraModalDec(TaskName='Digits') pdsl85.SaveNifti_PermTest(TaskName='Digits') pdsl58.IntraModalDec(TaskName='Dots') pdsl58.SaveNifti_PermTest(TaskName='Dots') pdsl58.IntraModalDec(TaskName='Digits') pdsl58.SaveNifti_PermTest(TaskName='Digits') pdsl58.SaveNifti_PermTest_Conj(TaskName='Dots') pdsl58.SaveNifti_PermTest_Conj(TaskName='Digits')</pre> <h3>Between-format decoding for 5-year-olds (Figures 4A, 5B):</h3> <pre>pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl5.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl5.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl5.CrossModalDec(TaskName1='Dots', TaskName2='Letters') pdsl5.SaveNifti_PermTest(TaskName='Dots2Letters') pdsl5.CrossModalDec(TaskName1='Letters', TaskName2='Dots') pdsl5.SaveNifti_PermTest(TaskName='Letters2Dots') pdsl5.SaveNifti_PermTest_Conj(TaskName1='Dots2Letters', TaskName2='Letters2Dots')</pre> <h3>Between-format decoding for 8-year-olds (Figure 4A):</h3> <pre>pdsl8.CrossModalDec(TaskName1='Dots', TaskName2='Digits') pdsl8.SaveNifti_PermTest(TaskName='Dots2Digits') pdsl8.CrossModalDec(TaskName1='Digits', TaskName2='Dots') pdsl8.SaveNifti_PermTest(TaskName='Digits2Dots') pdsl8.SaveNifti_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <h3>Between-format decoding, paired tests between 5- and 8-year-olds (Figure 4B)</h3> <pre>pdsl5.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots') pdsl8.SaveNifti_Paired_PermTest_Conj(TaskName1='Dots2Digits', TaskName2='Digits2Dots')</pre> <p> </p>
Stormwater filter water quality and quantity data - treatment of road runoff in the city of Vantaa, Finland
<p>The water quantity and quality dataset is from road runoff filters in the city of Vantaa, Finland. Data are from 6 campaigns in 2017 and 2019. The data is documented in: Koivusalo, H., Dubovik, M., Wendling, L., Assmuth, E., Sillanpää, N., Kokkonen, T. 2023. Performance of sand and mixed sand-biochar filters for treatment of road runoff quantity and quality. Accepted to Water. </p> <p> </p>
Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).</p> <p>This replication package contains the following files and folders:</p> <ul> <li><strong>README</strong><br> This file</li> <li><strong>MuseumsDk.csv</strong><br> The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li><em>museum</em>: Name of the museum. </li> <li><em>type</em>: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li><em>munic</em>: Municipality, in which the museum is located.</li> <li><em>yr</em>: Year of the observation.</li> <li><em>units</em>: Number of visit sites.</li> <li><em>resp</em>: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li><em>vis</em>: Number of (physical) visitors.</li> <li><em>aarc</em>: Number of articles published (archeology).</li> <li><em>ach</em>: Number of articles published (cultural history).</li> <li><em>aah</em>: Number of articles published (art history).</li> <li><em>anh</em>: Number of articles published (natural history).</li> <li><em>exh</em>: Number of temporary exhibitions.</li> <li><em>edu</em>: Number of primary school classes on educational visits to the museum.</li> <li><em>ev</em>: Number of events other than exhibitions.</li> <li><em>ftesc</em>: Scientific labor (full-time equivalents).</li> <li><em>ftensc</em>: Non-scientific labor (full-time equivalents).</li> <li><em>expProperty</em>: Running and maintenance costs [1,000 DKK].</li> <li><em>expCons</em>: Conservation expenditure [1,000 DKK]. </li> <li><em>ipc</em>: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li><strong>prepare_data.R</strong><br> This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li><strong>DataPrepared.csv</strong><br> This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li><strong>make_table_descriptive.R</strong><br> This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li><strong>IO_Ray.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>IO_Ray_ordering_outputs.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li><strong>allOrderings.rds</strong> (not included in the ZIP file, uploaded separately)<br> This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li><strong>IO_Ray_model_averaging.R</strong><br> This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>/tables/</strong><br> This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li><strong>/figures/</strong><br> This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>
Data from: Keep numbers in view: Red-eared sliders (Trachemys scripta elegans) learn to discriminate relative quantities
<p><span>The ability to discriminate relative quantities, one of the numerical competences, is considered as an adaptive trait in uncertain environments. Besides humans, previous studies have reported this capacity in several non-human primates and birds.</span><span> Here, we test whether red-eared sliders (<em>Trachemys scripta elegans</em>) can </span><span>discriminate different relative quantities</span><span>. Subjects were first trained to distinguish different stimuli with food reward. Then, they were tested with novel stimuli pairs to demonstrate how they distinguished the stimuli. The r</span><span>esults show that most subjects can complete the initial training and use relative quantity rather than absolute quantity to make choices during testing phase. This study provides behavioural evidence of relative quantity</span> <span>discrimination in a reptile species, and suggests that such capacity may be widespread among vertebrates.</span></p>
Data for: Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine lakes on the Tibetan Plateau
<p>The primary objectives of the study were to: (i) determine OM quantity and quality in the sediments of alpine lakes on the Tibetan Plateau, (ii) identify the primary environmental regulators of OM quantity and quality, and (iii) reveal OM transformations in the water column and sediments.</p> <p>Firstly, we collected sediments and lake water from 20 lakes with diverse morphology and sizes across the entire Tibetan Plateau.</p> <p>Secondly, We analyzed the bulk sedimentary OM and two leachable pools of the sedimentary OM, i.e., water-soluble OM and alkaline-extracted OM combining elemental and stable isotopic analysis, optical measurements (i.e., absorbance and fluorescence spectroscopy), and ultrahigh-resolution molecular techniques (i.e., electrospray ionization-assisted Fourier transform-ion cyclotron resonance mass spectrometry, ESI FT-ICR MS). We also measured the lake water physicochemical characteristics (i.e., depth, transparency, salinity, turbidity, DO, pH, TN, TP and Chl a), as well as the composition of dissolved OM in water column via optical measurements.</p> <p>Thirdly, we performed statistical analysis to determine the primary environmental control and predictors of the spatial variability in sedimentary OM on the Tibetan Plateau. The statistical analysis included non-parametric Kruskal-Wallis with Dunn post hoc test, redundancy analysis, and linear regression models.</p> <p>Main results of the study are that (i) sedimentary water-soluble OM and alkaline-extracted OM were both dominated by low-molecular-weight, low-aromaticity compounds with low contributions of terrestrial humic substances, suggesting that sedimentary leachable OM was primarily regulated by in-lake sources and processes; (ii) water depth, water transparency, and total phosphorus concentration in water column explained ~50% variance of sedimentary bulk and leachable OM, substantiating the importance of autochthonous sources and primary productivity in regulating the quantity and quality of sedimentary OM; (iii) in comparison to lake surface water DOM, water-soluble OM and alkaline-extracted OM from sediments had higher proportions of terrestrial humic-like substances, suggesting preferential preservation of allochthonous materials in the sediments.</p>
Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity
<p>This is data for the paper "Improving UAV-SfM photogrammetry for modeling high-relief terrain image collection strategies and ground control quantity". (<strong>DOI: </strong><a href="https://doi.org/10.1002/esp.5665">https://doi.org/10.1002/esp.5665</a>)</p> <p>This data is openly available, provided the original work is properly cited. The unzip Password can be found on the original paper in ESPL.</p>
CSV and JSON data describing the quantity and content of uploads to Thingiverse for 2015-2020
<p>The COVID-19 pandemic profoundly affected various aspects of daily life, particularly the supply and demand of essential goods, resulting in critical shortages. This included personal protective equipment (PPE) for medical professionals and the general public. To address these shortages, online "maker communities" emerged, aiming to develop and locally manufacture critical products. While some organized efforts existed, the majority of initiatives originated from individuals and groups on platforms like Thingiverse. This paper presents a longitudinal analysis of Thingiverse, one of the largest maker community websites, to examine the pandemic's effects. Our findings reveal a surge in community output during the initial lockdown periods in major contributing nations (primarily those in the western-hemisphere), followed by a subsequent decline. Additionally, throughout 2020, pandemic-related products dominated uploads and interactions during this period. Based on these observations, we propose recommendations to expedite the community's ability to support local, national, and international responses to future disasters.</p>
Comparison of Quality and Quantity of M-PRP Cellular Content Filgrastim vs. Pegfilgrastim
ClinicalTrials.gov study NCT05573386. IPD Sharing: NO. Countries: 1. Publications: 50.
The Impact of Relaxation Massage Before Bedtime on Sleep Quality and Quantity.
ClinicalTrials.gov study NCT06781866. IPD Sharing: YES. Countries: 1. Publications: 3.
Nesting chinstrap penguins accrue large quantities of sleep through seconds-long microsleeps
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Data from: Canine sense of quantity: evidence for numerical ratio-dependent activation in parietotemporal cortex
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Data from: Resource quantity and heterogeneity drive successional plant diversity in managed and unmanaged boreal forests
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Data from: Keep numbers in view: Red-eared sliders (Trachemys scripta elegans) learn to discriminate relative quantities
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Data from: Fungal and beetle diversity in deciduous fine woody debris in spruce-dominated forests in relation to substrate quantity and quality
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Quality-quantity tradeoffs drive functional trait evolution in a model microalgal “climate change winner”
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Data from: The quantity of deposited environmental DNA in plant-insect interactions depends on taxon, abundance, and interaction time
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Response to intruder number is related to quantity discrimination performance in a wild bird
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Data from: Data for habitat quality or quantity? Niche marginality across 21 plants and animals suggests differential responses between highland and lowland species to past climatic changes
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CSV and JSON data describing the quantity and content of uploads to Thingiverse for 2015-2020
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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.