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22,038 results for “relativity”
Algae, and Seagrass Coverage Relative to Presence of Sargassum in a Seagrass Meadow within Crandon Park, Florida, USA, March 2024 – March 2025
This package contains data on percent coverage of sargassum, seagrass, and algae recorded along 3 transects extending from the shoreline of Crandon Park, Florida from 2024-03-07 to 2025-03-26 as part of the Coastal Ecosystem-Research Experience for Teachers (CE-BIORETS) program. The data also include epiphyte-cover scores and water visibility, temperature, and salinity values. We placed a m^2 quadrat on the seafloor at 5-meter intervals along three 50-meter transects extending from the shoreline. We then recorded percent coverage of seagrass, algae, and sargassum taxa (to genus or species) and the epiphyte load within the quadrat. We also recorded water visibility, temperature, and salinity at the water surface along the transect. We collected these data to investigate the impact sargassum has on native seagrass meadows. Data collection for this project is complete
Red Wood-Ant Nests and Fault-Related Methane Micro-Seepage 2016
We measured methane (CH4) and stable carbon isotope of methane (ẟ13C-CH4) concentrations in ambient air and within a red wood-ant (RWA; Formica polyctena) nest in the Neuwied Basin (Germany) using high-resolution in-situ sampling to detect microbial, thermogenic, and abiotic fault-related micro-seepage of CH4. Methane degassing from RWA nests was not synchronized with earth tides, nor was it influenced by micro-earthquake degassing or concomitantly measured RWA activity. Two ẟ13C-CH4 signatures were identified in nest gas: −69‰ and −37‰. The lower peak was attributed to microbial decomposition of organic matter within the RWA nest, in line with previous observations that RWA nests are hot-spots of microbial CH4. The higher peak has not been reported in previous studies. We attribute this peak to fault-related CH4 emissions moving via fault networks into the RWA nest, which could originate either from thermogenic or abiotic CH4 formation. Sources of these micro-seepages could be Devonian schists, iron-bearing “Klerf Schichten,” or overlapping micro-seepage of magmatic CH4 from the Eifel plume. Given the abundance of RWA nests on the landscape, their role as sources of microbial CH4 and biological indicators for abiotically-derived CH4 should be included in estimation of methane emissions that are contributing to climatic change.
newbi4fmri2020 Variant2 Suboptimal Slow Event Related
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newbi4fmri2020 Variant5 Suboptimal Event Related
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Analytical Framework for Precise Relative Motion in Low Earth Orbits
<p>The data sets provided here can be used to recreate the plots of the paper “Analytical Framework for Precise Relative Motion in Low Earth Orbits” available at this <a href="https://arc.aiaa.org/doi/10.2514/1.G004716">link</a>.</p> <p>That paper presents a practical and efficient analytical framework for the precise modelling of the relative motion in low Earth orbits.</p>
Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024
This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.
Relative predation rates on juvenile Chinook Salmon in the lower Stanislaus River, California, 2012-2024 by habitat suitability informed by juvenile Chinook Salmon and black bass observations in the lower Stanislaus and Merced rivers, California, 2012-2017
Overview The purpose of this work was to estimate relative predation on juvenile Chinook Salmon rearing in tributaries of the San Joaquin River, California in relation to meso- and microhabitat factors. Predation rates were estimated using predation bioassays. Ranges of depth and velocity targeted by the bioassays were informed by habitat suitability indices developed prior to field efforts. Juvenile Chinook and Bass Habitat Suitability Indices The purpose of this dataset is to develop habitat suitability indices for juvenile Chinook Salmon (<120mm) on the lower Stanislaus River and nonnative black bass ( Micropterus spp.). on the lower Merced River, both tributaries of the San Joaquin. This data was used to identify target ranges of depth and velocity during predation fieldwork. Occupancy data was collected via snorkel surveys on the lower Stanislaus River in 2018 and 2019 and on the Merced River in 2012 and 2014-2017. Predation Tethering Bioassay Study The purpose of this field study was to estimate relative rates of predation of juvenile Chinook Salmon. Predation rates were estimated using assays of tethered hatchery Chinook Salmon deployed across a range of mesohabitats on the lower Stanislaus River. Habitat suitability was expected to vary across mesohabitats and across depths and velocities sampled within habitats. Cameras were deployed with tethers to identify predators for a subset of predation events happening within the first 1-2 hours of deployment. Assays were deployed monthly March-May in 2022 and 2024. A supplemental set of assays were deployed in May 2023 under wet water year conditions that varied strongly from conditions sampled in 2022 and 2024.
SBC LTER: Ocean: HFR-derived surface flow metrics, surface water retention times, and related factors in the Santa Barbara Channel (2012-2019)
This data package include three files: 1. daily maps of High-Frequency Radar (HFR) measured surface currents, indices of mesoscale eddy locations, and local retention times on a 2km grid; 2. monthly time series of wind stress, alongshore pressure gradient, surface current EOF principal components, vorticity, eddy area, eddy presence, and spatially averaged retention times from January 2012 to December 2019; 3. A MATLAB script for plotting the maps and timeseries. These data were processed in order to investigate the drivers of surface water retention in the Santa Barbara Channel, CA, details of which are available in the study: Brokaw, R.J., D.A. Siegel, and L. Washburn. Physical Drivers of Surface Water Retention in the Santa Barbara Channel. [In preparation for Journal of Geophysical Research: Oceans.]
Evidence accumulation relates to perceptual consciousness and monitoring
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ECOBREED WP4 soybean data related to Randelovic et al. (2020)
<p>Data related to the publication of Randelovic et al. (2020) Agronomy 10, 1108. doi: 10.3390/agronomy10081108. Data include the following files: (a) Excel file with two sheets (2018 & 2019) including trial information (plot allocation) and number of plants per square meter; (b) RGB image of soybean trial 2018 taken at V4 stage (four unfolded trifoliolate leaves); (c) RGB image of soybean trial 2018 taken at R3 stage (beginning pod); (d) RGB image of soybean trial 2019 taken at V4 stage; (e) RGB image of soybean trial 2019 taken at R3 stage. [Growth stages according to Fehr WR, Caviness CE (1977) Stages of soybean development. Iowa State Univ. Cooperative Ext. Serv., Spec. Rep. 80.]</p>
Lexical Relations from the Wisdom of the Crowd 1.0
<p>A set of 300 most frequent nouns has been extracted from the Russian National Corpus. Then, each method or resource, including RuThes, produced at most five hypernyms, if possible. In case it is not possible, missing answers treated as empty results. This resulted in 9 322 unique non-empty subsumption pairs that have been passed for crowdsourcing annotation on the Yandex.Toloka microtask platform. Each pair has been annotated by seven different annotators whose mother tongue is Russian and the age is at least 20 by February 1, 2017.</p> <p>The layout of the human intelligence task (HIT) design assumes the direct answer to a simple question: does the given pair of words represent a meaningful <em>is-a</em> relation? Since the crowd workers are not expert lexicographers and this question might be difficult for them, it has been rephrased as “Is it correct that a <em>kitten</em> is a kind of <em>mammal</em>?” (in Russian).</p> <p>The answers have been aggregated using the Yandex.Toloka proprietary answer aggregation mechanism. As the result, 3 940 out of 9 322 pairs have been annotated as positive while the rest 5 382 have been annotated as negative.</p> <p>Interestingly, the workers were more confident in negative answers rather than in the positive ones. These negative answers are extremely useful for both training and testing different relation extraction methods. To the best of our knowledge, this is the first dataset of this kind made for the Russian language using microtask-based crowdsourcing.</p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
Dataset of "Marcus cross relation in the space of H-atom abstraction reactions boosted through off-diagonal thermodynamics"
<p>Proton-coupled electron transfer (PCET) and hydrogen-atom transfer (HAT) reactions play critical roles in biological processes and modern organic synthesis. The kinetics of these processes can align with the principles described in the renowned Marcus cross relation (MCR), a framework initially formulated to describe electron transfer mechanisms. The MCR provides an outstanding link between the kinetics of PCET/HAT reaction involving two distinct reactants and two related auxiliary self-exchange reactions – each between a molecule of one of the reactants and its coupled radical. In this study, we investigate the applicability and limitations of the canonical MCR across over 300 PCET and HAT reactions, providing a comprehensive theoretical analysis. Our findings reveal the need for an enhanced framework that incorporates ‘off-diagonal’ thermodynamic factors—asynchronicity and frustration. Of these factors, asynchronicity, which quantifies the imbalance between the proton vs. electron transfer components of the reaction, is identified as the dominant contributor to the improved predictive accuracy of the MCR. Notably, the incorporation of off-diagonal thermodynamics yields a more pronounced enhancement for HAT reactions than for PCET reactions. This advancement offers a refined theoretical basis for understanding H-atom abstraction mechanisms and underscores the importance of off-diagonal effects in PCET/HAT chemistry.</p>
Dataset on Weather-related disasters in agriculture in Italy - WDA
<h1><strong>Abstract</strong></h1> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Pontrandolfi A, Alilla R, De Natale F, Nuti R, Parisse B, Pepe AG, Dataset on Weather-related Disasters in Agriculture (WDA) in Italy 2005–2021, Data in Brief <br><a href="https://doi.org/10.1016/j.dib.2025.111323">https://doi.org/10.1016/j.dib.2025.111323</a></p> <p>The database on Weather-related disasters in agriculture (WDA) is a part of the cloud storage which hosts the materials of the <a href="https://agrometeo.crea.gov.it/">Observatory for agricultural meteorology and climatology</a> of the Research Center for Agriculture and Environment belonging to the Council for Agricultural Research and Economics (CREA). The Observatory website has a specific section devoted to <a href="https://agrometeo.crea.gov.it/dati-e-analisi__trashed/rischio-meteorologico-in-agricoltura/">weather-related risk in agriculture</a>.</p> <p>A specific relational SQL database has been created fo data entry information from the official decrees of WDA declaration in Italy.</p> <p>From this relational SQL database, a <strong>dataset </strong>of WDA has been extracted for the period from 2005 to 2021 and here published</p> <p>The WDA dataset aims to make available useful data for weather-related risk assessment and analysis in the Italian agricultural sector.</p> <h2>Attached content:</h2> <ul> <li>pdf file "A_Description_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "DiscoveryMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>xlsx file "StructuralMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> </ul>
Catalog of NE Italy earthquakes Mw with related velocimetric time series
<p>Mw catalog (xlsx format) of earthquakes occurred in Norheastern Italy from 2016 to 2023; the catalog reports estimations for:</p> <ul> <li>ML (Bragato and Tento, 2005);</li> <li>Mw calculated from SA (Moratto et al., 2017);</li> <li>Mw calculated from MT (Moment Tensor; Saraò et al., 2021);</li> <li>The tgz file with the corrected velocimetric waveforms (SAC fomat with P and S arrival times used for the locations and units in m/s); tgz file can be found in Waveforms.tgz. EVDP SAC header is expressed in meters.</li> </ul> <p>Continuous raw time series can be dowloaded from Oasis website (Priolo et al., 2015).</p> <p> </p>
Dataset related to the manuscript: "An open-source integrated framework for the automation of citation collection and screening in systematic reviews"
<p>Dataset related to the manuscript: “An open-source integrated framework for the automation of citation collection and screening in systematic reviews”, to be used together with the code stored at https://github.com/AD-Papers-Material/BART_SystReviewClassifier to reproduce the results.</p> <p>There are three datasets:<br> - The Record data collected from the online scientific databases;<br> - The session journal which describes the search session, i.e., how many records were collected and from which source, for each query/session pairs.<br> - The session data which is the outcome of the classification and review tasks;</p>
Trajectory Design for Proximity Operations: The Relative Orbital Elements' Perspective
<p>The data sets provided here can be used to recreate the plots of the paper “Trajectory Design for Proximity Operations: The Relative Orbital Elements’ Perspective” available at this <a href="https://arc.aiaa.org/doi/full/10.2514/1.G006175">link</a>.</p> <p>That paper presents how to rigorously transform back-and-forth the equations of the relative motion in the close-range regime between Hill-Clohessy-Wiltshire and Relative Orbital Elements formulations. As straightforward application, it is presented a methodology to generate piecewise constant acceleration profiles from an impulsive guidance solution, setting up a control grid that minimizes the difference between impulsive and equivalent delta-v burns corresponding to the acceleration profile.</p> <p>Applications are implementation of autonomous guidance and control policies for close-range satellite proximity operations.</p>
Forestry roads in the Purapel fluvial catchment and related changes in sediment connectivity
<p>This dataset contains georeferenced data of forestry roads and sediment connectivity in the Purapel catchment, which drains the Chilean Coastal Range. The forestry road network consists of all the dirt and gravel roads mapped in QGIS by observing open satellite images and vectorial data available during January 2021. The observed data are maps that were listed in the QGIS OpenLayers plugin (<a href="https://github.com/sourcepole/qgis-openlayers-plugin">https://github.com/sourcepole/qgis-openlayers-plugin</a>), such as Google Satellite (Map data ©2015 Google) and OpenStreetMap <sup>1</sup>, the road network of the Chilean Congress National Library (<a href="https://www.bcn.cl/siit/mapas_vectoriales">https://www.bcn.cl/siit/mapas_vectoriales</a>) and compositions of Sentinel 2 images (European Space Agency, courtesy of the U.S. Geological Survey) of the post-2017 fire period.</p> <p>Sediment Connectivity maps were calculated on a 5 m resolution LiDAR DTM using the Connectivity Index<sup> 2</sup>. The maps were derived from the stand-alone, free and open-source executable SedInConnect 2.3<sup> 3</sup> using the Weighting factor of <sup>2</sup> and two different targets, which are available as tif files:</p> <ul> <li>ICs.tif contains <em>IC<sub>s</sub></em>, the Connectivity Index to the stream network.</li> <li>ICrs.tif contains <em>ICr<sub>s</sub></em>, the Connectivity Index to the road and the stream network.</li> </ul> <p>Here, the Road Connectivity, <em>RC </em>(dimensionless) is defined as the difference between both previous maps, with the aim to describe the change in sediment connectivity due to forestry road network:</p> <ul> <li><em>RC = IC<sub>rs</sub> - IC<sub>s</sub></em></li> </ul> <p>It is available as RC.tif file. The area of<em> high RC </em>was defined using the percentile 95 (3.12). File RC95.tif is a mask of <em>RC </em><em>≥</em><em> 3.12</em>.</p> <p>The contributing area <em>CA </em>(m<sup>2</sup>) was calculated using the multiple flow D-infinity approach <sup>4</sup> using TauDEM (https://hydrology.usu.edu/taudem/taudem5/downloads.html).</p> <p>The file CA_RC95.tif contains the contributing area (m<sup>2</sup>) of the surfaces with highest changes in sediment connectivity due to the road network. That is:</p> <ul> <li><em>CA_RC95 = </em>{<em>CA </em>|<em> RC </em><em>≥</em><em> 3.12</em>}</li> </ul> <p>The landscape distribution of those surfaces, in terms of proximity to the hilltops and valleys, is described by the density plot of the raster file CA_RC95.tif in R:</p> <pre><code>library("raster") library("ggplot2") CA_RC95<-raster("CA_RC95.tif") CA_RC95<-CA_RC95*0.0025 df = as.data.frame(CA_RC95) df = na.omit(df) ggplot(df,aes(CA_RC95)) + geom_histogram(aes(y=..count..*25),binwidth = 50)+ geom_density(aes(y=50 * ..count..*25), col="blue",size=2, adjust=10000)+ xlab("Contributing Area [ha] \n Hilltop Valley") + ylab("Area [m2]")+ theme(axis.text.x = element_text(face="bold", size=30), plot.title = element_text(color="black", size=40, face="bold",hjust=0.5), axis.title.x=element_text(color="blue", size=40, face="bold"), axis.text.y = element_text(face="bold", size=30), axis.title.y=element_text(color="blue", size=40, face="bold"))+ scale_y_continuous(trans = 'log10')+ ggtitle("Upstream area of surfaces with \n High Road Connectivity (RC > 3.12)") </code></pre> <p>Bibliography</p> <p>1. OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org/ (2017).</p> <p>2. Cavalli, M., Trevisani, S., Comiti, F. & Marchi, L. Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. <em>Geomorphology</em> <strong>188</strong>, 31–41 (2013).</p> <p>3. Crema, S. & Cavalli, M. SedInConnect: a stand-alone, free and open source tool for the assessment of sediment connectivity. <em>Computers and Geosciences</em> <strong>111</strong>, 39–45 (2018).</p> <p>4. Tarboton, D. G. A new method for the determination of flow directions and upslope areas in grid digital elevation models. <em>Water Resources Research</em> <strong>33</strong>, 309–319 (1997). </p>
CO₂ and related biogeochemical data from permafrost rivers on the Qinghai-Tibet Plateau (2016–2018, 2023)
This dataset includes four years of direct measurements from 50 permafrost rivers in the Qinghai-Tibet Plateau’s major Asian headwaters (Yellow, Yangtze, Lancang, Nu, Derung, Yarlung Tsangpo, Marja Tsangpo, and Indus Rivers), collected during the ice-free seasons (April–October) of 2016–2018 and 2023. It includes CO₂ partial pressure (pCO₂) and emission rates, concentrations of DOC, DIC, and major dissolved ions, carbon (δ¹³C), sulfur (δ³⁴S), and oxygen (δ¹⁸O) isotopes, alongside site location and other physiochemical data. Given its scarcity and scientific significance, this dataset will greatly support updates to global river CO2 flux estimates.
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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.