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Satellite-to-Ground QKD SKR dataset for P&M and Entanglement-based Protocols
<p>This dataset provides calculated Key Performance Indicators (KPIs) for satellite-to-ground quantum key distribution (QKD) links, modeled across Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO). The LEO orbit is modeled over a two-week period, while MEO and GEO orbits are modeled over a single day, whereas samples are provided with a sampling rate of 10 seconds.</p> <p>The satellite downlink channel is simulated using two QKD protocols: the Prepare-and-Measure protocol (Decoy-BB84) and the Entanglement-based protocol (BBM92). System specifications align with the LaiQa project source prototype, incorporating SNSPDs as the detection technology and assuming telescope-to-fiber coupling for ground reception.</p> <p>This dataset includes essential input metrics such as elevation angles for different orbits and various Optical Ground Stations (OGS) over time, along with key output metrics, including Secure Key Rates (SKR), Quantum Bit Error Rate (QBER), and Link Loss. Additionally, a comprehensive PDF guide is provided to assist with data handling and interpretation.</p> <p>Note: The results that are presented in the READ_ME file provide the volume of distilled keys and the number of distilled AES 256 keys over a time period of two weeks for different satellite orbits.</p>
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>
GERONTE H2020 project - GERDAT005 - Intrinsic capacity evaluation and intervention protocol
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a protocol for the evaluation of intrinsic capacity and frailty, with subsequent interventions aimed at optimizing health status and support for older patients with multimorbidity and cancer.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of Geronte is to take account of intrinsic capacity. Most older patients who are diagnosed with cancer also suffer from other illnesses and impairments that could affect their prognosis, priorities and ability to tolerate and benefit from treatment. For tailored oncologic decision making, it is essential to obtain a complete overview of the patient’s health status. This dataset contains the protocol for evaluation intrinsic capacity and potential interventions for impairments or vulnerabilities that were identified in this evaluation. It is going to be used in the assessment and management of older patients with multimorbidity and cancer, within the GERONTE care pathway.</p>
Comparison among transport layer protocols
<p>Data provide a comparison of transport layer protocols (TCP/MPTCP, UDP, SCTP, DCCP, QUIC, RTP) for railway applications in terms of "Congestion control", "Flow control", "Error control", "Connection establishment", "Addressing", "Checksum (for misdelivery)", "Multiplexing", "Connection-oriented", "Casting", "Stream/message-oriented", "Reliable", "Data bundling".</p> <p> </p>
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
Field data obtained from the implementation of the Field Protocols (1, 2, and 3) from WP3 in B-GOOD Project
<p>Dataset of the field data obtained from the implementation of the field protocols 1, 2, and 3, developed under B-GOOD (Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making, Grant agreement No. 81762) project in WP3.</p> <p>The primary goal of field data collection was to gather data that could be used for the validation of floral resources maps and models developed across the various tasks within WP3, and also to fill specific information gaps during the development of the phenological model. The fieldwork was conducted in three countries (Portugal, Belgium, and the United Kingdom) using three field protocols developed under B-GOOD and described in Milestone MS15.</p> <p>These protocols are part of Tasks 3.1 and 3.3 and serve various purposes. The primary goal of Field Protocol 1: "<em>Assessment of plant species composition on key landscape elements/habitats important for bees</em>" was to determine the species composition in selected key plant communities (<em>i.e.</em> landscape elements or habitats) important for bees. Field Protocol 1 was used to determine the plant species composition of specific ALMaSS landscape elements and to confirm/validate the plant composition of some BIOEUNIS habitat types, developed in Task 3.1. The main goal of Field Protocol 2: "<em>Assessment of Phenology of Floral Resources for Bees</em>" was to determine the flower phenology of targeted plant species to construct flowering phenological curves for targeted plant species. Field Protocol 2 was used to validate the floral resource models developed in Task 3.2 (see Section 2.4). The primary goal of Field Protocol 3: "<em>Floral resources evaluation (detailed method)</em>" was to conduct a detailed evaluation of the floral resources in each landscape window with B-GOOD mini-apiaries to map resource availability. Field protocol 3 was divided into two parts: Part 1 - “<em>Assessment and quantification of floral resources</em>”, aiming to determine the species composition, species cover, and flower abundance; and Part 2 - “<em>Flowering species characterization</em>”, aiming to quantify the number of flowers per individual plant, and the nectar and pollen production of target plant species. Field Protocol 3 was also used to make a detailed assessment of plant species composition and plant resources at the landscape level, as well as to fill the gaps in knowledge about pollen and nectar production of some target plant species. The field data collected by the implementations of the field protocols could be categorized into three main groups: Plant Species Composition (Field Protocol 1 and Field Protocol 3: Part 1), Phenology of Floral Resources (Field Protocol 2), and Flowering Species Characterization (Field Protocol 3: Part 2).</p> <p>Field protocols have been implemented in Portugal, the United Kingdom, and Belgium. Field protocols 2 and 3 were fully implemented in the three countries. However, field protocol 1 was not implemented as a stand-alone field protocol in Portugal and the United Kingdom due to logistical and time constraints primarily caused by the COVID pandemic. However, this does not hamper our ability to obtain landscape-specific plant composition data because the information gathered in this protocol can be derived entirely from the implementation of the first part of Field Protocol 3. As a result, for Portugal and the United Kingdom, Field Protocol 1 data was derived from the first part of Field Protocol 3. The full dataset gathered by the implementation of these three protocols is available here.</p> <p>The files “field-data-protocol-1-be.xlsx”, “field-data-protocol-1-pt.xlsx” and “field-data-protocol-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 1: "Assessment of plant species composition" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-2-be.xlsx”, “field-data-protocol-2-pt.xlsx” and “field-data-protocol-2-uk.xlsx” have the field data obtained from the implementation of Field Protocol 2: "Assessment of Phenology of Floral Resources" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-3-part-1-be.xlsx”, “field-data-protocol-3-part-1-pt.xlsx “and “field-data-protocol-3-part-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 1 “Assessment and quantification of floral resources” in Belgium, Portugal, and the UK, respectively.</p> <p>The file “field-data-protocol-3-part-2-be-pt-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 2 - “Flowering species characterization” in Belgium, Portugal, and the UK.</p> <p>For further details, see Alves da Silva et al. 2020. Field protocols for the assessment of Floral Resources. Milestone MS15 EU Horizon 2020 B-GOOD Project. GA No. 817622 and Ziółkowska et al 2022. Floral Resource Models Validation Deliverable D3.4 EU Horizon 2020 B-GOOD Project, GA No. 817622.</p>
NAHosMIP experimental protocol
<p>This package contains details of the experimental protocol for the North Atlantic Hosing Model Intercomparison Project (NAHosMIP). See https://www.tipes.dk/na-hosing-mip/ </p> <p>The experimental protocol is described in detail in Jackson et al, (2022), Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project. GMD [Full reference to come]</p> <p>The files attached are:</p> <p>create_hosing_GC3LL.py - python code to create a netcdf file with uniform hosing of a given rate (ie 0.3 Sv). This file is set up to read in grid data for HadGEM3-GC3-1LL</p> <p>create_hosing_GC3LL_grl_v2.py - python code to create a netcdf file with hosing around Greenland of a given rate (ie 0.1 Sv). This file is set up to read in grid data for HadGEM3-GC3-1LL and has some grid locations that are specific to the model.</p> <p>hosing.nc - example of uniform hosing of 0.3Sv for HadGEM3-GC3-1LL grid</p> <p>hosing_grl.nc - example of hosing around Greenland of 0.1Sv for HadGEM3-GC3-1LL grid</p> <p>check_hosing_GC31LL.py - script to check hosing file and code for compensation of the hosing throughout the volume.</p> <p>mesh_mask_HighresMIP_eORCA1_extr.nc - grid data for HadGEM3-GC3-1LL</p> <p>SSS_GC31LL.nc example surface salinity data for HadGEM3-GC3-1LL</p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Data From: The Oyster River Protocol: A multi assembler and kmer approach for de novo transcriptome assembly.
<p>Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a <em>de novo</em> transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at <a href="https://github.com/macmanes-lab/Oyster_River_Protocol">https://github.com/macmanes-lab/Oyster_River_Protocol</a>. Instructions for software installation and use, and other details are available at <a href="http://oyster-river-protocol.rtfd.org/">http://oyster-river-protocol.rtfd.org/</a>.</p>
MADIA_732678_SCRIBA_Bonding images and protocol_01
<p>ONLY METADATA</p> <p>Collection of protocol and images on bonding procedure of:</p> <ul> <li>PDMS on several substrates (glass, silicon wafer, SU-8);</li> <li>a polystyrene transparent top layer on sensors substrate.</li> </ul> <p>Data produced between from April 2017 to November 2017.</p>
The Impact of the COVID-19 Pandemic On Cities. A Scoping Review Protocol
<p>The aim of the scoping review is to map out evidence based research on the Covid-19 pandemic impact on the European cities. The review questions touch three broad areas of interest:</p> <ol> <li>the aspects of urban life described and analysed in publications on the impact of the pandemic on cities</li> <li>the aspects of urban life that are described in terms of crisis, breakdown, turnaround, etc. (crisis, disruption, slump, shift…) in such studies</li> <li>theoretical and methodological approaches applied in such studies</li> </ol> <p>The search was conducted in June 2022, with the final body of literature consisting of 3,994 publication references from EBSCOhost, APA Psyc, Scopus, Web of Science, Proquest, Wiley, Sage, JSTOR, Tailor&Francis, Oxford Journals databases (Fig. 1). The following English words were searched for in titles, abstracts and keywords in the databases: (pandemic OR ‘Covid-19’) AND (city OR cities OR urban*). We used the following criteria for articles to be included in the study: 1) peer and non-peer-reviewed empirical papers in journals published in English from January 2019 to June 2022; 2) included studies where the impact of COVID-19 pandemic on European city/cities was an explicit variable of interest; 3) contained analysis of empirical data on cities or urban life retrieved or collected within and explicitly addressing the COVID-19 pandemic; 4) addressed the social, cultural, economic, political and socio-geographical aspects of a city. We excluded from our sample papers that were: 1) theoretical and opinion literature, media press releases, reports, MA dissertations and PhD theses; 2) secondary research papers (reviews, meta-analyses); 3) papers not in English; 4) studies about non-European cities; 5) studies which do not explicitly address the impact of the COVID-19 pandemic on cities; 6) studies addressing a city as a variable of secondary importance; 7) studies outside the scope of the COVID-19 pandemic, published before December 2019; 8) studies not addressing the social or human aspects of urban life.</p> <p>The final database of coded documents consisted of 138 empirical articles presenting findings on the impact of the COVID-19 pandemic on European cities. </p>
Protocol for a systematic review: association between airborne pollen, intermittent allergic rhinitis and blood pressure
<p>Previous epidemiological studies have found an increased risk of cardiovascular morbidity and mortality following days with heightened pollen exposure and suggested that intermittent allergic rhinitis might be associated with blood pressure. Pollen sensitization and subsequent pollen exposure cause local inflammation and cytokine release in individuals with intermittent allergic rhinitis (pollen allergy). Inflammatory mediators can travel throughout the body, hence providing the physiologic basis by which pollen allergy may lead to systemic inflammation, which is known to be a risk factor for cardiovascular events. However, the findings regarding the potential association between intermittent allergic rhinitis, pollen exposure, and cardiovascular health are not fully conclusive. To date, no systematic review has been published on this topic.</p> <p>This systematic review seeks to answer: Are exposure to airborne pollen and intermittent allergic rhinitis associated with blood pressure? Secondary questions include: (1) Are there personal characteristics (sex, age) which modify a potential association between intermittent allergic rhinitis or pollen exposure with blood pressure and/or hypertension? (2) What research gaps exist in our understanding of how intermittent allergic rhinitis, pollen exposure, and cardiovascular health are interrelated?</p> <p>Published herein are:</p> <ul> <li>Protocol for the systematic review, including the search strategy</li> <li>Supplement 1: PROSPERO registration</li> <li>Supplement 2: Data extraction table</li> <li>Supplement 3: Risk of bias assessment strategy</li> <li>Supplement 4: Risk of bias assessment tool</li> </ul>
Dataset for "Phosphorus fractionation of recycled fertilizers reveals inadequacy of the SMT protocol"
<p>Raw dataset used in the manuscript "Phosphorus fractionation of recycled fertilizers reveals inadequacy of the SMT protocol"</p>
Interlaboratory testing of CuSO4 toxicity in the “Standardized Aquatic Microcosm” protocol consisting of multiple phytoplankton and animals in a chemically defined medium.
Four different laboratories conducted a total of ten experiments of the “Standardized Aquatic Microcosm” to test the reproducibility of results to control, low, medium, and high concentrations of CuSO4. In nine experiments, treatments consisted of six replicates of 0, 500, 1000, and 2000 ppb Cu++. One experiment, ME74, used 0, 127, 255, 509 ppb. The purpose was to test a chemically defined medium (thus negating differences due to local water supplies) and the same 10 species of phytoplankton and 5 animals, including Daphnia. Microbes were undefined. The protocol included the weekly re-introduction of small numbers of each species to allow potential recovery from toxicity. Control microcosms had a “spring algal bloom” terminated by zooplankton grazing and multiple competitive interactions. The copper inhibited some phytoplankton more than others and killed many grazers, especially Daphnia. The data set presented several interesting statistical properties that would yield new insights. (a) The results were very similar, but the timing varied— the higher the concentration of copper, the longer the inhibition and mortality of organisms, so those at 500 ppb recovered earlier, the 1000 ppb recovered later, and at 2000 ppb most never recovered. But if compared on each sampling day, e.g., 10, 14, … to 64, results appear highly variable. (b) In at least one experiment, the toxicity of copper was challenging to demonstrate statistically because high variability in the timing of recovery of the intermediate concentration increased pooled variances. (c) The elimination of highly-sensitive dominant organisms allowed less-sensitive organisms to increase in abundance. Within natural environments, the observation that some species increase in the presence of toxic substances has been used to discredit toxicity testing without considering the relative sensitivities of competing or predatory species. (d) The competitive interactions among organisms, e.g., cyanobacteria and green alga
Supporting data for "A simple ATAC-seq protocol for population epigenetics"
<p>This is supporting data for an article in which we describe a protocol for the generation of sequence-ready libraries for population epigenomics studies. The protocol is a streamlined version of the Assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) that provides a positive display of accessible, presumably euchromatic regions. The protocol is straightforward and can be used with small individuals such as daphnia and schistosome worms, and probably many other biological samples of comparable size, and it requires little molecular biology handling expertise.</p> <p>In "Agarose picture.Tif" the left lane shows the 100 bp size marker, first 10 bands from down to top: 100bp, 200bp, 300bp, 400bp, 500bp, 600bp, 700bp, 800bp, 900bp and 1kbp.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Video Analysis Scale of Engagement (VASE): Initial and Final Protocols
<p>These files contain the initial and final protocols that were used to develop the Video Analysis Scale for Engagement (VASE). The abstract of our article in Wellcome Open Research, 5:230: https://wellcomeopenresearch.org/articles/5-230/v2:</p> <p><strong>Background</strong>: The current study sought to develop a valid, reliable and unobtrusive tablet computer-based observational measure to assess engagement of people with advanced dementia. The Video Analysis Scale of Engagement (VASE) was designed to enable the rating of moment-by-moment changes in engagement during an activity, which would be useful for both future research and current residential care. <strong>Methods:</strong> An initial version of the VASE was tested. Face validity and content validity were assessed to validate an operational definition of engagement and develop an acceptable protocol for the scale. Thirty-seven non-professional and professional volunteers were recruited to view and rate level of engagement in music activities using the VASE. <strong>Results</strong>: An inter-class coefficient (ICC) test gave a high level of rating agreement across professionals and non-professionals. However, the ICC results of within-professionals were mixed. Linear mixed modelling suggested that the types of interventions (active or passive music listening), the particular intervention session being rated, time period of video and the age of raters could affect the ratings. <strong>Conclusions</strong>: Results suggested that raters used the VASE in a dynamic fashion and that the measure was able to distinguish between interventions. Further investigation and adjustments are warranted for this to be considered a valid and reliable scale in the measurement of engagement of people with advanced dementia in a residential care setting.</p>
Tissue-mimics example datasets for Icy protocols
<p>This upload contains three image datasets to test the Icy protocols P1 to P3 and plugin <a href="http://icy.bioimageanalysis.org/plugin/metrolospim/">metroloSPIM</a> described in Andilla, J., Jorand, R., Olarte, O. <em>et al.</em> Imaging tissue-mimic with light sheet microscopy: A comparative guideline. <a href="https://www.nature.com/articles/srep44939"><em>Sci Rep</em></a> <strong>7, </strong>44939 (2017). https://doi.org/10.1038/srep44939 </p> <p><strong>MCTS_H2B-FP.tif</strong>: tissue-mimics example dataset to test Icy protocol <a href="http://icy.bioimageanalysis.org/protocol/p1-detect-tissue-mimics-and-crop-3d-sub-volume/">P1 – Detect tissue mimics and crop 3D sub-volume</a></p> <p><strong>MCTS_H2B-FP_crop.tif</strong>: crop of MCTS_H2B-FP.tif (tissue-mimics example dataset) to test Icy protocol <a href="http://icy.bioimageanalysis.org/protocol/p2-snr-and-contrast-measurements/">P2 – SNR and contrast measurements</a> </p> <p><strong>MCTS_Beads.tif</strong>: fluorescent beads example dataset to test Icy protocol <a href="http://icy.bioimageanalysis.org/protocol/p3-characterize-the-psf-from-beads-in-3d/">P3 – Characterize the PSF from beads in 3D</a> </p>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Developing Learning Paths - The Learning Paths Protocol
<p>Learning Paths (LPs) are pathways that guide learners through a set of learning courses or materials to be undertaken progressively to acquire the desired knowledge and skills on a subject of interest. In this video, we explain how to develop learning paths step-by-step using the Learning Paths protocol. </p>
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</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.