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9,400 results for “Quality”
Annual Water Quality in Everglades National Park, Florida Bay, West Florida Shelf, and Florida Keys National Marine Sanctuary, Florida, USA: 1994-2019
Annual (water year basis) geometric mean concentrations of total phosphorus (TP), soluble-reactive phosphorus (SRP), total nitrogen (TN), dissolved inorganic nitrogen (DIN; calculated as nitrate + nitrite + ammonia), chlorophyll-a (Chl-a), and total organic carbon (TOC) concentrations across Everglades National Park (ENP), Florida Keys National Marine Sanctuary (FKNMS), West Florida Shelf and Florida Bay. This dataset is composed of data from multiple sources including Florida Coastal Everglades, Florida International University Southeast Research Center (FIU SERC), South Florida Water Management District (SFWMD), and National Oceanic and Atmospheric Administration Atlantic Oceanographic and Meteorological Laboratory (NOAA AOML). All values reported less than the laboratory minimum detection limit (MDL) were set to one-half the MDL. Annual geometric mean concentrations were computed for monitoring locations with greater than five years of data and four samples per year with a minimum of one sample in the wet and dry seasons. This dataset was created to evaluate long-term spatial and temporal trends in nutrients, chlorophyll-a, and total organic carbon at the landscape scale relative to freshwater and marine ecosystems.
Water Quality Data (Rainfall-driven autosampler) from the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, June 2003 - ongoing
Water quality samples are being collected using ISCO autosamplers at all freshwater sites: SRS1a (not active), SRS1c (not active), SRS1d, SRS2, and SRS3. Rain level actuators are used at the sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when a rain event at a site exceeds the threshold of 2.5 cm per hour, the autosampler at that site collects a 1000mL sample 30 minutes later. The samples are retrieved from the site every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. Salinity values were not taken consistently from 2000 to mid-2017; those values were replaced by -9999 in the data. See also Shark River Slough precipitation data package (knb-lter-fce.1092) and Shark River Slough extensive water quality data (knb-lter-fce.1072) on the FCE LTER website's data catalog or in the EDI repository.
Como and Phalen lake ChatBot water quality and recreation text survey, 2022 and 2023
We collected data from visitors to two urban lakes in Saint Paul, Minnesota, using a conversational chatbot to assess visitor perception of current lake water quality, trends in water quality over time, and other questions relevant to park managers. Data were collected at Como Lake in 2022 and 2023, and at Lake Phalen in 2023. Signs were installed at three locations around each lake with high pedestrian traffic. Each sign had a hook question (“How many watercraft are on the lake right now? Text the number to XXX-XXX-XXXX”). Visitors who responded to this question initiated a series of optional follow-up questions, using a conversational chatbot run by software that automates the sending and receiving of text messages. Survey questions included asking respondents about their primary purpose for visiting the lake today, how often they have visited the lake in the past 12 months, and whether they perceive that water quality in the lake is improving, getting worse, or remaining about the same. Respondents were asked to provide their ZIP code, used to estimate distance traveled to the lake. Respondents also had the opportunity to opt-in to future data collection via phone or text. An AI language model was then used to process the information and parse and synthesize responses. Unique anonymous identifiers were used to key survey response data.
Long-term trends and synchrony in dissolved organic matter characteristics in Wisconsin, USA lakes: quality, not quantity, is highly sensitive to climate
Dissolved organic matter (DOM) is a fundamental driver of many lake processes. In the past several decades, many lakes have exhibited a substantial increase in DOM quantity, measured as dissolved organic carbon (DOC) concentration. While increasing DOC is now widely recognized, fewer studies have sought to understand how characteristics of DOM (DOM quality) change over time. Quality can be measured in several ways, including the optical characteristics spectral slope (S275-295), spectral ratio (SR), absorbance at 254 nm (a254), and DOC-specific absorbance (SUVA; a254:DOC). However, long-term measurements of quality are not nearly as common as long-term measurements of DOC concentration. We used 24 years of DOC and absorbance data for seven lakes in the North Temperate Lakes Long Term Ecological Research site in northern Wisconsin, USA to examine temporal trends and synchrony in both DOC concentration and quality. We predicted lower SR and S275-295 and higher a254 and SUVA trends, consistent with increasing DOC and greater allochthony. DOC concentration exhibited both significant positive and negative trends among lakes. In contrast, DOC quality exhibited trends suggesting reduced allochthony or increased degradation, with significant long-term increases in SR in three lakes. Patterns and synchrony of DOM quality parameters suggest they are more responsive to climatic variations than DOC concentration. SUVA in particular tended to increase with greater moisture and decrease with drier conditions. These results demonstrate that DOC quantity and quality can exhibit different complex long-term trends and responses to climate components, with important implications for aquatic ecosystems.
CLA Yahara Lakes Citizen Offshore Water Quality Monitoring 2016 - 2017
In 2013, Clean Lakes Alliance (CLA) launched a Citizen Water Quality Monitoring pilot. Objectives included evaluating and tracking nearshore water quality conditions on all five Yahara lakes: Lakes Mendota, Monona, Waubesa, Kegonsa and Wingra. In 2016, in order to fully understand the interaction between the offshore and nearshore environment, CLA volunteers will begin sampling the deepest point (deep hole) of all Yahara lakes. The offshore monitoring program will focus on two components: water clarity sampling and dissolved oxygen and temperature measurement. Data from the offshore monitoring program will be compared to data from the nearshore program.
Plant recruitment and seed quality in the Black Sand extended growing season experiment for East Knoll, Audubon, Lefty, and Trough sites, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of plant recruitment and seed quality.
PIE LTER Year 2022 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, and depth at four sites in the Plum Island Estuary.
Four YSI EXO2 water quality sondes were deployed from June 2022 to October 2022 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, and depth in 15-minute intervals.
Dissolved Organic Carbon Concentration, Dissolved Organic Matter Optical Properties, and Water Quality Indicators in the Plum Island Estuary (PIE), Massachusetts, USA (2018-2023)
This is a data set of paired in situ measurements of water quality parameters, total suspended solids concentration, and concentration and optical properties (absorption coefficient spectra and fluorescence indices) of dissolved organic matter (DOM) collected between 2018 and 2023 in the Plum Island Estuary and nearshore waters. In situ water quality measurements (salinity, temperature, optical dissolved oxygen saturation, turbidity, and dissolved organic matter fluorescence) were collected with a water quality sonde from the surface (top 1 m of water column), along with corresponding samples that were processed and analyzed in the lab for dissolved organic carbon (DOC) concentration, chromophoric DOM (CDOM), absorption coefficient spectra, DOM excitation-emission matrix (EEM) fluorescence, and total suspended sediment (TSS) concentration. The data were used in multiple studies (see manuscripts listed below) focusing on the dynamics of DOC and CDOM in the Plum Island Estuary.
Taste Quality Representation in the Human Brain
Open the record for dataset details and reuse information.
MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research
<p>Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604</p>
Data to "Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G.^, Schepko, M.^, Klein, L. K., Paulun, V. C., and Fleming, R. W. (2021) Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback. Front. Neurosci. 14:591898.<br> doi: 10.3389/fnins.2020.591898</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2020.08.11.246173</p>
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
New Challenges in Point Cloud Visual Quality Assessment: A Systematic Review (Dataset)
<p>This dataset is a collection of annotated information on the scientific papers screened and analyzed for the systematic review of the literature in Point Cloud Visual Quality Assessment. </p> <p>The data is structured as follows:</p> <ul> <li>General information <ul> <li>Document title</li> <li>Authors</li> <li>Year of publication</li> <li>Venue (Conference or Journal title)</li> <li>Citations (number)</li> <li>URL/DOI</li> </ul> </li> </ul> <ul> <li>About the content <br> <ul> <li>Content Type: Point clouds (PC), Colored Point clouds (CPC), Meshes, Dynamic Point Clouds (DPC)</li> <li>Content source: Source of the content used in a subjective QA test or the evaluation of one or more QA metrics</li> </ul> </li> </ul> <ul> <li>About metric benchmarks <ul> <li>Subjective Ground-truth Data: Dataset(s) Source of the subjective scores used as ground-truth in a QA metric benchmark</li> <li>Assessed Metrics: Types of metrics assessed in a benchmark (JPEG standards, IQM, NR, State-of-the-art, others)</li> <li>Performance Measures: PLCC, SROCC, KRCC, RMSE, OR, others</li> </ul> </li> </ul> <ul> <li>About Objective QA metrics <ul> <li>Metric: Name given to the metric introduced in this paper</li> <li>Base: 3D-based or Projection-based</li> <li>Categories: Categories that characterize the approach of the proposed metric (Feature-based, Learning-Based, Perceptual-based, IQM, others) </li> <li>Reference: Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR)</li> </ul> </li> </ul> <ul> <li>About Subjective QA experiments <ul> <li>Display: Type of display (2D, 3D, AR, MR, VR) and interaction approach (passive, interactive, 3DoF, 6DoF) used in the described experiment.</li> <li>Rendering: Type of rendering used to display the stimuli (Points, Squares, Cubes, Surface)</li> <li>Lab/Remote: The experiment was run in one or more lab environments, or remotely (Lab, Cross-Lab, Remote)</li> <li>Rating: Subjective rating methodology used in the experiment (ACR, DSIS, PWC, others)</li> <li>Dataset: Name of the new subjective dataset if the experiment's results were published.</li> <li>Observers: Number of observers </li> <li>Distortion type: Types of distortions applied to the stimuli and assessed in the experiment</li> </ul> </li> </ul>
Quality-Assurance Package for the "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" Manuscript
<h2>Background</h2> <p>Neuroimaging research requires consistent image quality and temporal signal stability, especially for functional magnetic resonance imaging (MRI) studies that rely on detecting subtle blood-oxygen-level-dependent (BOLD) signal changes. Regular MR system performance monitoring is essential, especially for longitudinal and multi-site studies. This study aims to establish a robust quality assurance (QA) protocol to promote data comparability across scanner models, vendors, and sites, as well as over a prolonged period.</p> <p>The manuscript titled "<em>Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework</em>" was submitted to the Special Issue <a href="https://link.springer.com/journal/10334/updates/26638300">Reproducibility and Quality Assurance</a> of the Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA) journal.</p> <p>This QA package proposed by the manuscript hosts materials for</p> <ul> <li>all reconstructed images,</li> <li>instruction for data acquisition,</li> <li>instruction for image reconstruction,</li> <li>instruction for post-processing,</li> <li>example raw data and DICOM images, and</li> <li>images and scripts for T1/T2 fitting.</li> </ul> <p>The detailed information is listed below.</p> <h2>All reconstructed images</h2> <p>This directory contains all reconstructed images from the fBIRN phantom on three Siemens 3T scanners (Trio, Prisma.Fit, and Cima.X) and one GE (UHP) 3T scanner. It contains four sub-folders for each scanner. And each sub-folder contains (some of) the following sub-folders:</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product spin-echo (SE) images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h2>Instruction for data acquisition</h2> <p>This directory includes the following documents:</p> <ul> <li><code>write_QA_Tran_EPIrs.m</code> to generate the <code>QA_epi.seq</code> file for EPI scans.</li> <li><code>write_QA_Tran_T1.m</code>: to generate the <code>QA_T1.seq</code> file for SE scans.</li> <li><code>20241122_QA_protocol_instruction_siemens.docx</code>: standard operating procedure for QA measurements.</li> <li><code>QA_record.xlsx</code>: Excel sheet for the record of QA measurements.</li> </ul> <h2>Instruction for image reconstruction</h2> <h3><em>Documents</em></h3> <ul> <li><code>pulseq2mrd_epi.m</code>: convert GE Pulseq EPI raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_epi.seq</code> file.</li> <li><code>pulseq2mrd_se.m</code>: convert GE Pulseq SE raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_T1.seq</code> file.</li> <li><code>siemens2mrd_epi.m</code>: convert Siemens Pulseq EPI raw data (<code>.dat</code>) to MRD raw data (<code>.h5</code>) using the information in the <code>.dat</code> raw data.</li> </ul> <ul> <li><code>default.xml</code>: Gadgetron configuration file for SE image reconstruction. This document is already in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/default.xml</code>.</li> <li><code>qc_epi.xml</code>: Gadgetron configuration file for EPI image reconstruction, which is modified from the <code>default epi.xml</code> located in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/</code>.</li> </ul> <ul> <li><code>specialCard_ICE.png</code>: Special card setting for ICE online reconstruction.</li> </ul> <h3><em>Procedures for Gadgetron offline reconstruction</em></h3> <p><strong>Step 1: Gadgetron installation (for more details, visit <a href="https://gadgetron.github.io/tutorial/">here</a>)</strong></p> <ul> <li>Download and install <a href="https://www.docker.com/">Docker</a> software. You may need to install/update the Windows Sub Linux (WSL) system for the Docker installation.</li> <li>Open your terminal (Power shell with administrative privilege in Windows) and navigate to the folder you would like to map to the Gadgetron Docker container.</li> <li>Run: <code>docker run -t --name gt_latest --detach --volume ${pwd}:/opt/data ghcr.io/gadgetron/gadgetron/gadgetron_ubuntu_rt_nocuda:latest</code>. If docker is not recognized, set <code>docker</code> to connect to <code>C:\Program Files\Docker\Docker\resources\bin</code> in the Environment Path in Windows. This will download and then launch the <a href="https://gadgetron.readthedocs.io/en/latest/building.html">latest Gadgetron version</a> in a Docker container. It will also mount your current folder as a data folder inside the container.</li> <li>Run this command: <code>docker exec -ti gt_latest /bin/bash</code>. This will execute your Gadgetron container.</li> </ul> <p><strong>Step 2: Data preparation</strong></p> <ul> <li>Place your SE/EPI <code>.dat</code>/<code>.h5</code> data in the mounted folder.</li> <li>Run the command in Terminal: <code>cd /opt/data</code> to enter the mounted folder.</li> </ul> <p><strong>Step 3: MRD conversion</strong></p> <ul> <li>For Siemens data, you can convert the <code>.dat</code> data to MRD data by using Gadgetron. If Gsdgetron doesn't work (e.g. for XA EPI data), you can then use the Matlab script <code>siemens2mrd_epi.m</code>.</li> <li>The command for Siemens SE data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -o se_data.h5</code>.</li> <li>The command for Siemens EPI data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -m IsmrmrdParameterMap_Siemens.xml -x IsmrmrdParameterMap_Siemens_EPI.xsl -o epi_data.h5</code>.</li> <li>For GE data, you can convert the <code>.mat</code> raw data to MRD data by using the Matlab scripts with the corresponding <code>.seq</code> files. For SE conversion: use <code>pulseq2mrd_se.m</code> with <code>QA_T1.seq</code>. For EPI conversion: use <code>pulseq2mrd_epi.m</code> with <code>QA_epi.seq</code>.</li> </ul> <p><strong>Step 4: Gadgetron reconstruction</strong></p> <ul> <li>SE reconstruction: <code>gadgetron_ismrmrd_client -f se_data.h5 -c default.xml -o se_out.h5</code>.</li> <li>EPI reconstruction: first, put <code>qc_epi.xml</code> to the mounted folder and then copy it to the Gadgetron container: <code>cp /opt/data/qc_epi.xml /opt/conda/envs/gadgetron/share/gadgetron/config/</code>. Then, run the reconstruction: <code>gadgetron_ismrmrd_client -f epi_data.h5 -c qc_epi.xml -o epi_out.h5</code>.</li> </ul> <p><strong>Step 5: Load Gadgetron-reconstructed images (<code>.h5</code>)</strong></p> <ul> <li>Load SE <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_se_out.h5' ;</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_se_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_se_im = reshape(pulseq_se_im, [256, 256, 11, 2]) ;</p> </blockquote> <ul> <li>Load EPI <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_epi_out.h5';</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_epi_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_epi_im = reshape(pulseq_epi_im, [64, 64, 27, 200]) ;</p> </blockquote> <h3><em>Procedures for ICE online reconstruction</em></h3> <p>Before executing the Pulseq-based sequences, you can enable ICE online Reconstruction following the procedures below:</p> <ul> <li>Navigate to the Special Card (<code>specialCard_ICE.png</code>), set <code>Data handling</code> to <code>ICE STD</code> for NUMARIS/X (e.g. XA60A and XA61A), and <code>ICE 2D</code> for NUMARIS/4 (e.g. VB, VD, and VE).</li> <li>Select <code>Sum-of-Square</code> for coil combination.</li> <li>Be sure that the maximal pixel intensity does not violate the intensity threshold of <strong>4096</strong>.</li> </ul> <h2>Instruction for post-processing</h2> <p>The example post-processing is based on the reconstructed images from Cima.X over five days.</p> <h3><em>Reconstructed images from Cima.X</em></h3> <p><strong>Note</strong>: All <code>se</code> folders contain a <code>structuralQuality_main.m</code> to call the <code>structuralQuality.m</code> function for structural quality analysis. All <code>epi</code> folders contain a <code>temporalQuality_main.m</code> to call the <code>temporalQuality.m</code> function for temporal quality analysis.</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product SE images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h3><em>QA analysis Matlab package: </em><code><em>QA_functions</em></code></h3> <ul> <li><code>circfit.m</code>: to find the center point and radius of the phantom.</li> <li><code>makeCircleMask.m</code>: to make a circular mask based on the center point and radius.</li> <li><code>structuralQuality.m</code>: to analyze the structural quality of the SE images.</li> <li><code>temporalQuality.m</code>: to analyze the temporal quality of the EPI images.</li> </ul> <h3><em>Post-processing procedures</em></h3> <ul> <li>Step 1: Add the <code>QA_functions</code> folder to your Matlab Path.</li> <li>Step 2: Run the <code>temporalQuality_main.m</code> or <code>structuralQuality_main.m</code> script in each folder to produce the QA results of all reconstructed images inside the folder.</li> <li>Step 3: Run the <code>make_figure_epi.m</code> and <code>make_figure_se.m</code> to produce some of the tables and figures used in the manuscript.</li> </ul> <h2>Example raw data and DICOM images</h2> <p>The data and DICOM images were acquired from Cima.X on the fBIRN phantom on 06.08.2024.</p> <ul> <li>DICOM folder: contains the DICOM images for four EPI scans (the first two scans for warm-up) and two SE scans.</li> <li><code>meas*.dat</code>: Siemens raw data of two EPI scans for temporal quality analysis and two SE scans for structural quality analysis.</li> <li><code>*data.h5</code> files: the ISMRMRD data of the four raw datasets.</li> <li><code>*out.h5</code> files: the images reconstructed by Gadgetron.</li> <li><code>*.nii</code>: the NIFTI-format reconstructed images.</li> <li><code>siemens2mrd_epi.m</code>: to convert the Siemens EPI raw data to ISMRMRD data.</li> <li><code>read_image.m</code>: to convert the Gadgetron-reconstructed h5-format images to NIFTI-format images.</li> </ul> <h2>Images and scripts for T1/T2 fitting</h2> <p>This package includes DICOM images and T1/T2 fitting scripts for the fBIRN phantom. Images for T1 fitting were acquired using a product turbo spin echo sequence with an inversion recovery pulse (repetition time = 4000 ms, echo train length = 4). Images for T2 fitting were obtained using a product SE sequence (repetition time = 3500 ms). Both measurements were conducted on the Siemens Prisma.Fit 3T scanner on 05.06.2024.</p> <ul> <li><code>T1 sub-folder</code>: contains all DICOM images for T1 fitting with inversion recovery times of {50, 150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 2200, 3000} ms.</li> <li><code>T2 sub-folder</code>: contains all DICOM images for T2 fitting with echo times of {7.5, 15, 30, 45, 60, 75, 90, 130, 200, 250} ms.</li> <li><code>Do_T1fit.m</code>: Matlab script for T1 fitting.</li> <li><code>Do_T2fit.m</code>: Matlab script for T2 fitting.</li> </ul> <p>For more information regarding Pulseq and the workflow for data acquisition and image reconstruction, please visit our GitHub repositories: <a href="https://github.com/pulseq/pulseq">Pulseq Matlab software</a>, <a href="https://github.com/pulseq/tutorials">Pulseq Tutorials</a>, and <a href="https://github.com/pulseq/Pulseq-Rocks-2023-24-ISMRM-Reproducibility-Challenge">Pulseq Rocks for the 2024 ISMRM Reproducibility Team Challenge</a>.</p> <p>If you need any further information or have any questions, please feel free to contact our Pulseq email address: pulseq.mr@uniklinik-freiburg.de.</p>
Artificial Intelligence for Quality Control of manufacturing operations: Macro-mechanical milling in the Pilot Line GAMHE 5.0.
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of their parts is very important given the high requirements to which they are subject. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to apply Artificial Intelligence-based kits/solutions that provide in-process estimation to predict quality from some measured variables. </p> <p>The main goal of these datasets is to monitor the final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information using Artificial Intelligence-based solutions. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Weather and Air Quality data for Ireland as RDF data cube
<p>Weather, Air Pollution and Events data represented as RDF data cube. The original weather data has been downloaded from https://www.met.ie//climate/available-data/historical-data and the Air Quality data from <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm</a> and <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm</a>. The Events data refers to random events within the Republic of Ireland.</p> <p>The data has then been uplifted by running the {eeaMapping, metMapping, eventsMapping}.py scripts, which generate R2RML mappings to convert the CSV data to RDF. The mappings re-use vocabularies and ontologies that are W3C recommendations for dataset descriptions (DCAT, https://www.w3.org/TR/vocab-dcat-2/), statistical data (RDF Data Cube, https://www.w3.org/TR/vocab-data-cube/) and provenance data (PROV-O, https://www.w3.org/TR/prov-o/). The scripts use the R2RML engine from https://github.com/chrdebru/r2rml to execute the mappings which generate a data and metadata files for each of the datasets.</p> <p> </p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
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.