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23,351 results for “Comparative”
CLDF dataset derived from Huber and Reed's "Comparative Vocabulary" from 1992
<p>Cite the source of the dataset as:</p> <blockquote> <p>Huber, R. Q. and Reed, R. B. 1992. Vocabulario comparativo: palabras selectas de lenguas indígenas de Colombia [Comparative vocabulary. Selected words from the indigenous languages of Columbia]. Santa Fé de Bogota: Asociación Instituto Lingüístico de Verano.</p> </blockquote>
CLDF dataset derived from Heath et al's "Dogon Comparative Wordlist" from 2016
<p>Cite the source of the dataset as:</p> <blockquote> <p>Moran, Steven & Forkel, Robert & Heath, Jeffrey (eds.) 2016. Dogon and Bangime Linguistics. Jena: Max Planck Institute for the Science of Human History. (Available online at http://dogonlanguages.info, Accessed on 2024-08-12.)</p> </blockquote>
CLDF dataset accompanying Brid et al.'s "Comparative Wordlist for the Languages of the Gran Chaco Area" from 2022
<p>Cite the source of the dataset as:</p> <blockquote> <p>Brid, Nicolás, Cristina Messineo, and Johann-Mattis List (2022): A Comparative Wordlist for the Languages of the Gran Chaco Area. Leipzig: Max Planck Institute for Evolutionary Anthropology.</p> </blockquote>
Evolution of FDA Guidelines on Control of Nitrosamine Impurities in Human Drugs – A Comparative Analysis of September 2024 Revisions
<p>Nitrosamine impurities have become a significant concern in the pharmaceutical industry due to their carcinogenic potential. In response, the U.S. Food and Drug Administration (FDA) has continuously updated its guidelines to ensure the safety and efficacy of drug products. This review article provides a comprehensive analysis of the evolution of FDA guidelines on the control of nitrosamine impurities, with a particular focus on the September 2024 revisions. By comparing the latest guidance with previous versions, this article highlights key changes, including the expanded focus on Nitrosamine Drug Substance-Related Impurities (NDSRIs), updated risk assessment strategies, and the introduction of new Acceptable Intake (AI) limits. The analysis underscores the FDA's commitment to enhancing drug safety through rigorous control measures and global harmonization efforts.</p>
CLDF dataset derived from Kleinewillinghöfer's "Bikwin-Jen Comparative Wordlist" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Kleinewillinghöfer, Ulrich (2015). Bikwin-Jen Group. https://www.blogs.uni-mainz.de/fb07-adamawa/adamawa-languages/bikwin-jen-group/. Accessed on: 2020-04-15.</p> </blockquote>
CLDF dataset derived from Othaniel's "Jen Cluster Comparative Wordlist" from 2017
<p>Cite the source of the dataset as:</p> <blockquote> <p>Othaniel, Nlabephee Kefas. 2017. A phonological comparative study of the Jen language cluster. (MA thesis, Jos: Theological College of Northern Nigeria; 1–83pp.)</p> </blockquote>
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>
Kam-Niger-Congo comparative word list
<p>This is a comparative word list containing data collected with the Leipzig-Jakarta word list, intended to compare basic vocabulary between Kam and other Niger-Congo languages. It contains reconstructions for a variety of proto-languages already available in the literature (e.g. Jukunoid, Mumuyic, Proto-Bantu, Proto-Gbe, Proto-Potou-Akanic, and Proto-Fula-Sereer), as well as the author's own quasi-reconstructions for Niger-Congo, Benue-Congo, and Delta-Cross and cognate judgements.</p>
Comparative analysis of ADC values between high-cost (US331-000005-030PA) and low-cost (B07YZLCSRP) depth sensors
<p>The dataset provides a comparison between an expensive depth sensor, the "US331-000005-030PA", and a cheaper sensor, the "B07YZLCSRP". The dataset includes the ADC values from both sensors as well as the offset between them.</p> <p>Data were collected using an autonomous underwater profiler called s-Nautilus at the Real Club de Regatas de Cartagena. During this test, the s-Nautilus profiler was moved to various depths, and the time and 12-bit ADC values from both sensors were recorded.</p> <p>The recorded variables include:</p> <ul> <li><strong>timestamp UNIX (s):</strong> the timestamp indicating the date and time of each measurement.</li> <li><strong>hours (hh:mm:ss):</strong> time of recording of each measurement.</li> <li><strong>incr_time (s): </strong>cumulative time increment for each measurement.</li> <li><strong>ADC cheap sensor (unit of ADC of 12 bits): </strong>12-bit ADC values of depth sensor "B07YZLCSRP" at various depths of the s-Nautilus.</li> <li><strong>ADC expensive sensor (unit of ADC of 12 bits):</strong> 12-bit ADC values of depth sensor "US331-000005-030PA" at various depths of the s-Nautilus.</li> <li><strong>ADC difference (unit of ADC of 12 bits):</strong> difference in ADC values between the two sensors.</li> <li><strong>ADC + offset (unit of ADC of 12 bits):</strong> ADC values of depth sensor "B07YZLCSRP" adjusted by calculated offset.</li> <li><strong>average ADC differences (unit of ADC of 12 bits):</strong> average offset ADC for all measurements.</li> </ul>
Supplementary Material for "A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters"
<p>This is a file containing supplementary material for the paper <em>A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters.</em> For each star in target globular clusters, it lists crucial information on the linelist analyzed. In particular:</p> <ol> <li>Star ID.</li> <li>Chemical element.</li> <li>Wavelength.</li> <li>log <em>gf</em></li> <li>Excitation potential.</li> <li>Measured equivalent width with uncertaintiy.</li> </ol>
The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf
<p>This dataset contains the measured hourly mean microclimate and surface energy flux data from a field experiment. The experiment consisted of three treatments: unirrigated artificial turf, unirrigated natural turf, and irrigated natural turf (4 mm/day, 13:00-13:23 local time). The experiment was conducted from 2024-01-28 to 2024-03-18 in Burnley, Melbourne, Australia.<br><br>For each treatment, the measured hourly mean data included albedo, soil moisture content, air temperature, vapour pressure of water, wind speed, black globe temperature, mean radiant temperature, universal theraml climate index, wet-bulb globe temperature, soil temperature, turf surface temperature, incoming and outgoing longwave and shortwave radiant fluxes, sensible heat flux, latent heat flux, and ground heat flux. <br><br>Turf surface temperature, and incoming and outgoing longwave and shortwave radiant fluxes were measured at 1.5 m above ground surface.<br>Air temperature and vapour pressure of water were measured at 0.6 and 1.1 m above ground surface.<br>Wind speed, black globe temperature, mean radiant temperature, universal thermal climate index, and wet-bulb globe temperature were measured at 1.1 m above ground surface.<br>Soil moisture content, soil temperature and ground heat flux were measured at 0.1 m below ground surface.<br>Sensible heat flux and latent heat flux were calculated using the Bowen ratio-energy balance method.<br><br>Additionally, the hourly mean background weather conditions (air temperature and cloud amount) from the nearest public climate station in the study period were included in 'ReferenceClimateStation.csv'. Hourly total rainfall data measured at the study site was also included. <br><br>The aims of this study was to:<br>1. Compare the microclimate and human heat stress among the three treatments.<br>2. Assess and compare the human skin burn risks of the three treaments from their turf surface temperatures.<br>3. Analyse the surface energy fluxes of the three treatments to identify the mechanisms by which artificial turf develops any microclimate, human heat stress and turf surface temperature differences.<br><br>This study was published in:</p> <p><span>Cheung, P. K., & Livesley, S. J. (2025). The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf. <em>Building and Environment</em>, 112679. https://doi.org/10.1016/j.buildenv.2025.112679<br></span><br>Contact person: Dr Paul Cheung (cheung.p@unimelb.edu.au)</p>
Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars
<p>Supporting data for peer-reviewed publication entitled: 'Photometric detection of internal gravity waves in upper main-sequence stars. IV. Comparable stochastic low-frequency variability in SMC, LMC, and Galactic massive stars', published in A&A. For the purpose of open access, the authors have applied a CC BY licence to the author accepted manuscript version and made it publicly available: <a href="https://arxiv.org/abs/2410.12726">https://arxiv.org/abs/2410.12726</a></p> <p>Evolutionary models and stability window calculations courtesy of Jermyn et al. 2022 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ac4e89">10.3847/1538-4357/ac4e89</a>) are publicly available via: <a href="https://github.com/adamjermyn/conv_trends">https://github.com/adamjermyn/conv_trends</a></p> <p>TESS full-frame image data are publicly available from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute (STScI): <a href="https://archive.stsci.edu/missions-and-data/tess">https://archive.stsci.edu/missions-and-data/tess</a></p> <p>TESS light curves (provided in this repository) were extracted using the publicly available tglc (Han & Brandt 2023; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/acaaa7">10.3847/1538-3881/acaaa7</a>) software package: <a href="https://github.com/TeHanHunter/TESS_Gaia_Light_Curve">https://github.com/TeHanHunter/TESS_Gaia_Light_Curve </a></p> <p>SLF variability parameters (provided in this repository; cf. Tables 1 and 2 of the paper) were obtained using GP regression with the publicly available celerite2 (Foreman-Mackey et al. 2017; DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-3881/aa9332">10.3847/1538-3881/aa9332</a>) software package: <a href="https://celerite2.readthedocs.io/en/latest/">https://celerite2.readthedocs.io/en/latest/</a> and confidence intervals were obtained using the publicly available pymc3 (Salvatier et al. 2016; <a href="https://doi.org/10.7717/peerj-cs.55">https://doi.org/10.7717/peerj-cs.55</a>) software package: <a href="https://github.com/pymc-devs/pymc">https://github.com/pymc-devs/pymc</a></p> <p>This research was supported in part by the National Science Foundation (NSF) under Grant Number NSF PHY-1748958; the Research Foundation Flanders (FWO) with grant agreement numbers 1286521N, 11F7120N, and V411621N; UK Research and Innovation (UKRI) in the form of a Frontier Research grant under the UK government's ERC Horizon Europe funding guarantee (SYMPHONY; grant number: EP/Y031059/1); a Royal Society University Research Fellowship (URF; grant number: URF\R1\231631); and the KU Leuven Research Council (grant number C16/18/005: PARADISE).</p>
The monthly operating costs (vehicle, fuel, and maintenance) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The monthly operating costs of each compared vehicle were:</p> <ul> <li> <p>for the model, Tesla 3 electric vehicle was US$426.10/month, including purchase and depreciation US$333/month, fuel (electricity) US$27.8/month, maintenance US$65.3/month.</p> </li> <li> <p>for the model, Infiniti Q50, the internal combustion engine car was US$583.7/month, including purchase and depreciation US$321/month, fuel US$166/month, maintenance US$96.7/month.</p> </li> </ul>
Greenhouse gas emissions (lifecycle) of each compared vehicle, Tesla 3 (283 HP), and Infiniti Q50 (300 HP).
<p>We compared two vehicles with similar horsepower, Tesla 3 (283 HP), and Infiniti Q50 (300 HP). The CO_2 emissions for these vehicles were: </p> <ul> <li> <p>for the model Tesla 3, CO_2 emissions were 161.8 gCO_2 eq/mile, including 31.8 gCO_2 eq/mile in vehicle production, 25 gCO_2 eq/mile in battery production, and 105 gCO_2 eq/mile in electricity production.</p> </li> <li> <p>for the model Infiniti Q50, CO_2 emissions were 503.8 gCO_2 eq/mile , including vehicle production 40.5 gCO_2 eq/mile, fuel production 91.3 gCO_2 eq/mile, in-service combustion 372 gCO_2 eq/mile.</p> </li> </ul>
CLDF dataset derived from Carling's "Diachronic Atlas of Comparative Linguistics" from 2017
<p>Cite the source of the dataset as:</p> <blockquote> <p>Carling, Gerd (ed.) 2017. Diachronic Atlas of Comparative Linguistics Online. Lund: Lund University. (DOI/URL: https://diacl.ht.lu.se/). Accessed on: 2019-02-07.</p> </blockquote>
Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification
<p>Data files, chromatograms, and metadata for the Frontiers in Plant Science article "Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification" . </p> <p>doi: 10.3389/fpls.2021.693739</p>
Comparable respiratory activity in attached and suspended human fibroblasts
<p>Zdrazilova L, Hansikova H, Gnaiger E (2021) Comparable respiratory activity in attached and suspended human fibroblasts. MitoFit Preprints 2021.7. <a href="http://dx.doi.org/10.26124/mitofit:2021-0007">doi:10.26124/mitofit:2021-0007</a></p> <p>All respirometric data are expressed in SI units and are made available here Open Access.</p>
Improving Methods to Measure Comparable Mortality by Cause - Gold Standard Verbal Autopsy Data 2011-2014
<p>These data were collected and compiled as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) project, funded by Australia's National Health and Medical Research Council (NHMRC). Verbal autopsies (VAs) were conducted between 2011 and 2014 in three sites: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Diagnostic criteria and cause lists similar to those employed in the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. This study added 3512 deaths (2491 adults, 320 children, and 701 neonates) to the GS VA database created from the PHMRC study. This dataset contains the combined PHMRC and IMMCMC data for an updated GS VA database.</p>
Dataset: Comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research.
<p>Supplementary material for a comparative evaluation of a keyword based search and semantic search in a data portal for biodiversity research. We conducted a relevance evaluation with 6 users over 19 search questions in two search interfaces.</p> <p>The users provided up to five search questions and relevant keywords from their research background. We setup a dataset search over a corpus of ~92,000 randomly selected metadata files from GFBio (<a href="https://www.gfbio.org">https://www.gfbio.org</a>). For each of their own search queries, the users got two result sets presented. The first one displayed results obtained from a keyword search. The second panel contained dataset results from a prototypical semantic search. Instead of results with exact mentions of the query terms, the semantic search also presented related results with synonyms and more specific terms or terms obtained from concept nodes of a higher hierarchy level.</p> <p>Each user rated the relevance of his/her own search queries on a 7-point Likert scale for both search results.<br> In addition, users also assessed the expanded keywords for each question.</p> <p>More information can be found in our publication:</p> <p>Löffler, F. and Klan, F. (2016): Does Term Expansion Matter for the Retrieval of Biodiversity Data? in Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS'16), co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016),2016, <a href="http://ceur-ws.org/Vol-1695/paper2.pdf">http://ceur-ws.org/Vol-1695/paper2.pdf</a></p> <p> </p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
ScienceDex guides
Understand access before you commit
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.