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766 results for “Baseline”
Fig. 1 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 1. Peat swamp forest in Zamrud National Park.
Baseline Performance and MAS reaction to network Anomalies in Demo 1
<p>Collected Latency data and packet captures for the demo described by the paper 10.5281/zenodo.12820942 "Augmented Reality App with AI-based Pervasive Latency Monitoring of RAN and Programmable Metro Packet-Optical Networks" presented during ICTON24 conference.</p>
Nasopharyngeal Carriage, Antimicrobial Resistance, and Serotype Distribution of Streptococcus pneumoniae in Children Under Five in Lebanon: Baseline Data Prior to PCV13 Introduction
<p>Dataset and R Code Script</p>
Meteorological Data from Kardamyla, Chios: March 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from March 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.1°C, recorded on March 31st, 2024, at 15:10.</li> <li><strong>Lowest temperature</strong>: 3.3°C, recorded on March 24th, 2024, at 05:50.</li> <li><strong>Highest daily rainfall</strong>: 28.2 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 77.2 km/h, recorded on March 12th, 2024, at 00:10.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Meteorological Data from Kardamyla, Chios: February 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from February 1st to February 29th, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from February 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for February 2024</h3> <ul> <li><strong>Highest temperature</strong>: 21.9°C, recorded on February 29th, 2024, at 13:40.</li> <li><strong>Lowest temperature</strong>: 3.0°C, recorded on February 4th, 2024, at 07:30.</li> <li><strong>Highest daily rainfall</strong>: 49.4 mm, recorded on February 12th, 2024.</li> <li><strong>Highest wind speed</strong>: 82.1 km/h, recorded on February 11th, 2024, at 21:30.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Meteorological Data from Chios: March 2024 Baseline Measurements for the MUSICA Project
<p>The present meteorological data is collected from the weather station in <strong>Chiostown</strong>, located in Chios, and is published on the <strong>Zenodo</strong> platform for open access. The station is positioned at an elevation of 32 meters and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data presented here for March 2024 provides insight into the weather conditions leading up to the installation of the project's platform. Continuous monitoring efforts will track changes in weather and climate conditions as the project progresses.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.3°C, recorded on March 31st, 2024, at 15:50.</li> <li><strong>Lowest temperature</strong>: 7.1°C, recorded on March 24th, 2024, at 03:20.</li> <li><strong>Highest daily rainfall</strong>: 25.4 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 66.0 km/h, recorded on March 12th, 2024, at 10:50.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>
Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.
<p>Forest plot: Associations between BMI at baseline and pCR following NACT – stratification according to ER status.</p>
Mu-rhythm modulation associated with the observation and execution of a goal-directed action in healthy toddlers estimated using two different baseline conditions
<p>The database includes mu-rhythm ERD/ERS values elicited by the observation and execution of a goal-directed action in healthy toddlers (N=19). ERD/ERS values has been computed using to different baseline conditions: 1) observation of a black and white static image (BL1) and 2) short period of stillness (BL2).</p> <p>The first sheet of the database refers to action execution (AE) data, whereas the second sheet refers to action observation (AO) data. The database table in each sheet includes for each subject single trial ERD/ERS values (table rows) registered in 7 different scalp sites (table columns) for both BL1 and BL2.</p> <p>The database was used for the statistical analysis of the following paper: Piazza, C.; Visintin, E.; Reni, G.; Montirosso, R. The Effect of Baseline on Toddler Event-Related Mu-Rhythm Modulation. <em>Brain Sci.</em> <strong>2021</strong>, <em>11</em>, 1159. https://doi.org/10.3390/brainsci11091159 (<a href="https://www.mdpi.com/2076-3425/11/9/1159/htm">https://www.mdpi.com/2076-3425/11/9/1159/htm</a>)</p> <p>All the details about data acquisition and preprocessing are reported in the above mentioned publication.</p>
Baseline concentrations, spatial distribution and origin of trace elements in marine surface sediments of the northern Antarctic Peninsula
<p>Supplementary data to article published in Marine Pollution Bulletin 187 (2023) 114501: https://doi.<br> org/10.1016/j.marpolbul.2022.114501</p>
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
<p>Linear instability and quasilinear fluxes calculated with the <a href="https://genecode.org">GENE</a> plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/wikis/home">QuaLiKiz</a> transport model, as described in <a href="https://iopscience.iop.org/article/10.1088/1361-6587/ab5ae1">P. Mantica et al (2019) Plasma Physics and Controlled Fusion 62 014021</a>. <br> <br> The quasilinear fluxes were calculated with a bespoke saturation rule calibrated to dedicated GENE nonlinear simulations carried out in the same ITER regime. The quasilinear flux dataset was fit with a neural network (NN) regression model, which was then used for ITER baseline integrated modelling and performance projections. Alongside the linear stability dataset, the two separate quasilinear datasets correspond to an unfiltered dataset, and a filtered and data-augmented dataset used for the NN regression. For full details, please see reference [J. Citrin et al (2023) <em>submitted to Physics of Plasmas</em>]. <br> <br> The dimensionless input and output variables correspond to the IMAS gyrokinetic IDS standards. The major radius was taken as the reference length. A key and further details are found below.</p> <table> <caption><strong>Description of CSV file columns</strong></caption> <tbody> <tr> <td>rhoN</td> <td>Normalized toroidal flux coordinate</td> </tr> <tr> <td>ky</td> <td>Binormal wavenumber, normalized to the reference (ion scale) gyroradius</td> </tr> <tr> <td>omt_DT</td> <td>Normalized logarithmic main ion temperature gradient (<span class="math-tex">\(R/L_{Ti}\)</span>)</td> </tr> <tr> <td>omt_el</td> <td>Normalized logarithmic electron temperature gradient (<span class="math-tex">\(R/L_{Te}\)</span>)</td> </tr> <tr> <td>omn_el</td> <td>Normalized logarithmic electron density gradient (<span class="math-tex">\(R/L_{ne}\)</span>)</td> </tr> <tr> <td>s</td> <td>Magnetic shear</td> </tr> <tr> <td>q</td> <td>Safety factor (q-profile)</td> </tr> <tr> <td>gamma</td> <td>Instability growth rate (gyroBohm normalisation with IMAS standard)</td> </tr> <tr> <td>omega</td> <td>Instability frequency (gyroBohm normalisation). Positive frequencies correspond to the ion diamagnetic direction</td> </tr> <tr> <td>kperp2</td> <td>Square of perpendicular wavenumber weighted over poloidal mode structure <span class="math-tex">\(\langle{k_\perp^2}\rangle\)</span></td> </tr> <tr> <td>Q_DT</td> <td>ky-dependent ion heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>Q_el</td> <td>ky-dependent electron heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>G_el</td> <td>ky-dependent electron particle flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>QDT</td> <td>Quasilinear ion heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Qe</td> <td>Quasilinear electron heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Ge</td> <td>Quasilinear electron particle flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>QDT_ITG</td> <td>Quasilinear ion heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_ITG</td> <td>Quasilinear electron heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_ITG</td> <td>Quasilinear electron particle flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>QDT_TEM</td> <td>Quasilinear ion heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_TEM</td> <td>Quasilinear electron heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_TEM</td> <td>Quasilinear electron particle flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> </tbody> </table> <p> </p>
Brief introduction to parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China
<p>This short vidio is used to briefly introduce the contents of the article "Parameterization of downward longwave radiation based on long-term baseline surface radiation measurements in China".</p>
D2.1 - Demonstrator baseline and market characteristics report - Transcripts of interviews with a French company
<p>This is the transcript of the interviews with a French company for D2.1 "Demonstrator baseline and market characteristics report" defining the current baseline and the target/improved circular business models for two demonstrators and analyzing both demonstrators´ market characteristics and their impact on the target circular business models.</p>
Trained models for KGML-xDTD and baseline models
<p>This repository contains the trained models and relevant data of <em>KGML-xDTD </em>and some baseline models that are used to run the code under "./model_evaluation" stored on <a href="https://github.com/chunyuma/KGML-xDTD">Github</a>.</p>
D2.1 - Demonstrator baseline and market characteristics report - Consumer survey data set
<p>This is the data set for D2.1 "Demonstrator baseline and market characteristics report" defining the current baseline and the target/improved circular business models for two demonstrators and analyzing both demonstrators´ market characteristics and their impact on the target circular business models.</p> <p> </p>
Supplemental Files to "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are supplemental files to the manuscript, "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs". Supplements contain excel spreadsheets, DNA alignments, Newick tree files, FigTree files, and csv files.</p>
Baseline data for Citizen Energy Responsible Behaviour in 27 Member States
<p>This dataset contains all the information on the levels that each country in Europe should have in the labelling system to assess citizens energy behaviours fully described in the document AURORA D1.1 Near-Zero Emissions Citizens Label https://doi.org/10.5281/zenodo.7594879</p> <p>The zip file contains one document for each member state. Reference values are extracted from the same datasources individuated in D1.1 for the 5 selected countries fully described in the document.</p>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
Chum salmon baseline for the Western Alaska Salmon Stock Identification Program
<p>Uncertainty about the magnitude, frequency, location, and timing of the nonlocal harvest of sockeye and chum salmon was the impetus for the Western Alaska Salmon Stock Identification Program. The program was designed to use genetic data in mixed stock analysis to reduce this uncertainty. A baseline of allele frequencies in spawning populations is required for use in mixed-stock analysis to estimate the stock of origin of harvested fish. This report describes the methodology used to understand the population genetic structure among chum salmon populations and to build and test a baseline for use in mixed stock analysis of chum salmon. Of the 35,921 fish from 434 collections selected to be genotyped, the final baseline was composed of 32,817 fish from 402 collections representing 310 populations. Average population sample size was 106 fish. Reporting groups were determined through a combination of stakeholder needs and identifiability using genetic information, as measured using proof tests. The final reporting groups included Asia, Kotzebue Sound, Coastal Western Alaska, Upper Yukon River, Northern District (Alaska Peninsula), Northwest District (Alaska Peninsula), South Peninsula (Alaska Peninsula), Chignik/Kodiak, and East of Kodiak.</p>
A Study of Ramucirumab (LY3009806) Versus Placebo in Participants With Hepatocellular Carcinoma and Elevated Baseline Alpha-Fetoprotein
ClinicalTrials.gov study NCT02435433. IPD Sharing: YES. Countries: 20. Publications: 9.
Safety and Efficacy of Pre-defined, Fixed Dose of Gonal-f Pen Based on Subject Baseline Characteristics in Subjects Undergoing in Vitro Fertilization (IVF)
ClinicalTrials.gov study NCT00249834. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.