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861 results for “comparison data”
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-ic-fiducial)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>initial condition data for the fiducial simulations as well as the primordial phases used for all simulations</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-HR)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV HR simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-1024Mpc-z0)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV 1024Mpc simulation at z = 0</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.15eV-HR-z1)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.15eV HR simulation at z = 1</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Raw Data for the article: Right vs left portal branch puncture in TIPS creation with controlled expansion covered stent: comparison of hemodynamic and clinical outcomes
<p><strong>Purpose: </strong>To retrospectively compare outcomes of TIPS performed by puncturing left portal vein (LPV) vs right portal vein (RPV) to access the portal system.</p> <p><strong>Materials and methods: </strong>One hundred ninety-three consecutive patients underwent TIPS with controlled expansion covered stent by using the LPV (37 patients) or the RPV (156 patients). Patients were followed until the last clinical evaluation, liver transplantation, or death.</p> <p><strong>Results: </strong>Demographics and clinical characteristics of the two groups were comparable. The median follow-up was 9.6 months (range 0.1-50.6). Portosystemic pressure gradient (PSG) before TIPS 15.7 mmHg ± 4.7 in RPV group (RPVG) vs 15.4 mmHg ± 4.5 in LPV group (LPVG) (p = 0.725). After TIPS, PSG 6.3 mmHg ± 2.8 in RPVG vs 6.2 mmHg ± 2.2 (p = 0.839). In LPVG, the stent was dilated to 8-mm in 95% of patients vs 77% of RPVG (p = 0.015). Two (5.4%) and 22 (14%) patients underwent TIPS revision in LPVG and RPVG (p = 0.15). The incidence of overt HE was 13% in LPVG and 24% in RPVG (p = 0.177). Rebleeding occurred in 3 of 49 patients (6%) with variceal bleeding as an indication: 2/41 patients (4.9%) in RPVG vs 1/8 patients (12.5%) in LPVG (p = 0.417). Among 126 patients with refractory ascites 20 patients (15.9%) needed paracentesis 3 months after the procedure: 18/101 patients (17.8%) in RPVG vs 2/25 patients (8%) in LPVG (p = 0.231). Thirty-seven patients (19%) died: 32 (21%) in RPVG and 5 (14%) in LPVG (p = 0.337).</p> <p><strong>Conclusion: </strong>Compared with RPV puncture, in TIPS created through the LPV, the targeted PSG was reached with a smaller stent diameter. However, no significant difference in clinical outcomes was observed.</p>
A multi-system comparison of forecast flooding extent using a scale-selective approach [data]
<p>A multi-system comparison of forecast flooding extent using a scale-selective approach, data.</p> <p>Creator: Helen Hooker[1] Publication Year: 2023</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- CSM validation maps for forecast flood maps from Food Foresight (FF), GloFAS Rapid Flood Mapping (RFM) and FFWC Super Model (FFWC) predicting flooding extent on 25 July 2020 for the Jamuna River, Bangladesh compared against Sentinel-1 SAR-derived flooding extent. File format is e.g. CSM_FF_forecastdate_rundate.tif. CSM maps for Flood Foresight following system improvement are labelled with ..._reas.tif.</p> <p>Helen Hooker. (2023). A multi-system comparison of forecast flooding extent using a scale-selective approach (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7509980</p> <p>Related publications:</p> <p>A multi-system comparison of forecast flooding extent using a scale-selective approach; 2023; Hydrology Research (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
data set regarding to article Comparison of diagnostic value of SARC-F and its four modified versions in Polish community-dwelling older adults
<p>This data set corresponds with the article titled Comparison of diagnostic value of SARC-F and its four modified versions in Polish community-dwelling older adults</p>
MJO-QBO Model Inter-comparison Data
<p>Data in support of the paper "The Lack of a QBO-MJO Connection in Climate Models with a Nudged Stratosphere" by Zane K. Martin, Isla R. Simpson, Pu Lin, Clara Orbe, Qi Tang, Julie M. Caron, Chih-Chieh Chen, Hyemi Kim, L. Ruby Leung, Jadwiga H. Richter, and Shaocheng Xie, currently in preparation for submission.</p> <p>Data is organized by model, then ensemble member, then the temporal data resolution.</p> <p>Daily data are daily model OLR (olr/) and precipitation (precip/) in lat/lon/time format, over at least the tropical region spanning all longitudes and 20N to 20S. Daily data also include the Real-time Multivariate MJO index (RMM; RMM_index/) value from each model and ensemble members. OLR and precip files are provided on a 2.5 x 2.5 degree similar grid, rather than the models' native grid.</p> <p>Monthly data are temperature (temp/, at all vertical levels and all longitudes, from at least 20N to 20S, and the 100 hPa temperature file, as described more in the paper) zonal wind (at all vertical levels, and the 50 hPa wind file; wind/), and TEM vertical velocity (wtem/).</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Supplementary data for the manuscript titled 'Energy metabolism and aerobic respiratory chain of Vitreoscilla sp. C1: Comparison with beta-proteobacteria'
<p>This dataset contains the supplementary data files for the manuscript titled, 'Energy metabolism and aerobic respiratory chain of Vitreoscilla sp. C1: Comparison with beta-proteobacteria.' </p>
The Raw Data Of wound healing percentage on day 5, 10 and 15 of the control, comparison and 10% of subfraction ethyl acetate ointment from meniran leaves
<p>The Raw Data Of wound healing percentage on day 5, 10 and 15 of the control, comparison and 10% of subfraction ethyl acetate ointment from meniran leaves</p>
Data and code for "Cell shape characterization, alignment and comparison using FlowShape"
<p><strong>Code</strong></p> <p>This folder contains a snapshot of the FlowShape Python package at the time of paper submission. The latest version is available at: <a href="https://bitbucket.org/pgmsembryogenesis/flowshape/src/main/">https://bitbucket.org/pgmsembryogenesis/flowshape/src/main/</a></p> <p><strong>Data</strong></p> <p>Contain 3D meshes of <em>C. elegans</em> early embryo cells, in Wavefront .obj format. Each filename has three parts sepparated by underscores</p> <ul> <li>Embryo identifier</li> <li>Cell name</li> <li>Timestep For example: <code>wt13_ABar_18</code> Embryo = wt13 Cell = ABar Timestep = 18</li> </ul> <p>The timesteps are frames from the original timelapse, where one frame corresponds to 90 seconds. The starting point is arbitrary, so time is aligned by EMS division.</p> <p>Embryo labels:</p> <ul> <li><code>7cell01-7cell07</code>: 7 wild-type embryos imaged only around the seven-cell stage.</li> <li><code>wt01-wt19</code>: 19 wild-type embryos imaged for a longer time.</li> <li><code>dshmig01-dshmig05</code>: 5 dsh-2 / mig-5 RNAi knockdown embryos, imaged around seven-cell stage.</li> </ul> <p><code>cell_filters.csv</code> is a CSV file containing identifiers of embryos left out of the analysis because of automatically detected errors. Further, wt06 was also left out because of problems with labeling the cells.</p> <p><strong>Images</strong></p> <p>Contains raw microscopy data that was used to generate the meshes. <code>resolution.csv</code> contains a list of Z, Y and X resolutions (micron / pixel).</p> <p><strong>Changelog</strong></p> <p>0.1.0</p> <ul> <li>Initial upload</li> </ul> <p>0.2.0</p> <ul> <li>Added microscopy images.</li> <li>Removed cells 6 and 7 from dsh-2 / mig-5 dataset, which were not supposed to be included there.</li> <li>Removed Python cache files.</li> </ul>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-figure)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>data needed for generating all figures</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Data from: A comparison of non-destructive visceral swab and tissue biopsy sampling methods for genotyping-by-sequencing in the freshwater mussel Fusconaia askewi
<p>Limiting harm to organisms via genetic sampling is an important consideration for rare species. Nondestructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the Unionid freshwater mussel, Texas Pigtoe (<em>Fusconaia askewi</em>). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared to swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence) while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.</p>
Comparison tables for evaluating FAIR Digital Object and Linked Data
<p>RO-Crate and tables from the paper "<em>Evaluating FAIR Digital Object and Linked Data as distributed object systems</em>" <a href="https://doi.org/10.48550/arXiv.2306.07436">https://doi.org/10.48550/arXiv.2306.07436</a></p> <p>We systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself.</p> <ul> <li>table1.html<br> Considering FDO and Web according to the quality levels of the Interoperability Framework for Fast Data (Delgado 2016)</li> <li> <p>table2.html<br> Mapping the Metamodel concepts from the Interoperability Framework for Fast Data (Delgado 2016) to equivalent concepts for FDO and Web.</p> </li> <li> <p>table3.html<br> Checking FDO guidelines (Bonino et al. 2019; Anders et al. 2023) against its current implementations as DOIP (DONA 2018) and Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022), with suggestions for required additions</p> </li> <li> <p>table4.html<br> Comparing FAIR Digital Object (with the DOIP 2.0 protocol (DONA 2018)) and Web technologies (using Linked Data) as middleware infrastructures (Zarras 2004)</p> </li> <li> <p>table5.html</p> <p>Assessing RDA’s FAIR Data Maturity Model (FAIR Data Maturity Model Working Group 2020; Bahim et al. 2020) (first 2 columns) against the FDO guidelines (Bonino et al. 2019), FDO implemented with the protocol DOIPv2 (DONA 2018), Linked Data Platform (LDP) (Bonino da Silva Santos, Guizzardi, and Sales 2022) and examples from Linked Data practices in general. (— indicates Unspecified, may be possible with additional conventions)</p> </li> </ul> <p>A web rendering of this RO-Crate is available at <a href="https://w3id.org/ro/doi/10.5281/zenodo.8075229">https://w3id.org/ro/doi/10.5281/zenodo.8075229</a></p>
Data from: Comparison of carbon and nitrogen accumulation rate between bog and fen phases in a pristine peatland with the fen-bog transition
<p>Long-term carbon and nitrogen dynamics in boreal peatlands are affected by both vegetation production and decomposition processes. Here, we examined the carbon accumulation rate (CAR), nitrogen accumulation rate (NAR) and δ<sup>13</sup>C, δ<sup>15</sup>N of plant residuals in a peat core dated back to ~8500 cal yr BP in a temperate peatland in Northeast China. Impacted by the tephra during 1160 and 789 cal yr BP and climate change, the peatland changed from a fen dominated by vascular plants to a bog dominated by <em>Sphagnum mosses</em>. We used the Clymo model to quantify peat addition rate and decay constant for acrotelm and catotelm layers during both bog and fen phases. Our studied peatland was dominated by <em>Sphagnum fuscum</em> during the bog phase (789 ~ -59 cal yr BP) and lower accumulation rates for the upper sections in the acrotelm layer during this phase, suggesting the dominant role of volcanic eruption in the CAR of the peat core. Both mean CAR and NAR were higher during the bog phase than during the fen phase in our study, consistent with the results of the only one similar study in the literature. Because the input rate of organic matter was considered to be lower during the bog phase, the decomposition process must have been much lower during the bog phase than during the fen phase and potentially controlled CAR and NAR. During the fen phase, CAR was also lower under higher temperature and summer insolation, conditions beneficial for decomposition. δ<sup>15</sup>N of <em>Sphagnum </em>hinted that nitrogen fixation had positive effect on nitrogen accumulation, particular in recent decades. Our study suggested that decomposition is more important for carbon and nitrogen sequestration than production in peatlands in most conditions and if future climate changes or human disturbance increase decomposition rate, carbon sequestration in peatlands will be jeopardized.</p>
Data from: leaf functional diversity and environmental filtering in a tropical dry forest: comparison between two geological substrates
<p><span>The role of geological substrate in shaping plant community functional diversity remains poorly understood. Considering the involvement of leaves in the energy, water and nutrient economics of plants, we hypothesized that leaves experience geology-related filtering, impacting functional attributes and functional leaf community diversity on different substrates. We studied tropical dry forest communities on limestone and siliciclastic phyllite-derived soils, comparing their functional diversity and soil physico-chemical properties. We predicted the most benign habitat (less severe filter) to be associated with higher leaf functional diversity and an acquisitive strategy prevalence, while the more stressful habitat should show conservative leaf traits and lower leaf functional diversity. We measured six traits in 31 common tree species (representing ~80% of community crown cover): leaf area, specific leaf area, leaf thickness, leaf dry matter content, petiole length, and leaf blade narrowness. Leaf functional diversity was assessed through the functional trait dispersion metric. Intraspecific functional variation was examined in 25 species shared between substrates. The limestone substrate was more fertile (higher phosphorous) with higher water retention, while phyllite had higher nitrogen and lower humidity. Principal component analysis segregated plots by substrate, with limestone plots being more clustered. Community leaf functional diversity was higher in the limestone forest. Most species examined showed inter-substrate trait differences in at least one leaf functional trait. The two substrates constituted distinct growth environments, with the more benign substrate associated to higher community functional leaf diversity. The intraspecific analysis revealed the prevalence of acquisitive traits in the more benign and more conservative traits in the more stressful habitat. This study advances our understanding of the role of geological substrate as an environmental filter in tropical dry forest, influencing leaf functional responses and emphasizing the importance of intraspecific functional variation.</span></p>
ScienceDex guides
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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.