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1,773 results for “packaging”
Perry et al. (2025) Data Package: Effects of diluted bitumen and remediation methods on lower trophic levels within boreal lake enclosures. Data were collected during 2019 at the IISD Experimental Lakes Area in Northwestern Ontario.
This data package corresponds to a research study by Perry et al. (2025) titled "The effects of diluted bitumen, the shoreline cleaner Corexit EC9580A, and bio-stimulation on the lower food web of a boreal lake, with a focus on natural phytoplankton communities." The study examines the effect of controlled spills of diluted bitumen and two remediation methods on lower trophic levels (phytoplankton, periphyton, zooplankton). The study was undertaken within shoreline enclosures within Lake 260 at the IISD Experimental Lakes Area during 2019. In addition to primary oil recovery using sorbent pads, the two secondary remediation methods: 1) enhanced monitoring natural recovery (eMNR) that included the biostimulation of microbial communities via a slow release nutrient fertilizer, and 2) a shoreline washing agent (SWA or SCA; Corexit 9580) used to increase oil removal from affected shorelines. This data package includes the response of perphyton and zooplankton.
FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment
<p>To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology.</p> <p><a href="http://www.nature.com/articles/s41597-022-01429-9">https://www.nature.com/articles/s41597-022-01429-9</a> - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al.</p> <p>Quick start with the dataset in README.txt (<em>included in the newest version of the dataset</em>)</p> <p>Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability and reusability of experimental tribological data beyond typical publication practices.</p> <p><a href="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a> - Generating FAIR Research Data in Experimental Tribology - Get Scientific Results Ready for ML</p> <p><a href="https://doi.org/10.5281/zenodo.5720626">https://doi.org/10.5281/zenodo.5720626</a> - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment</p> <p><a href="https://doi.org/10.5281/zenodo.5720198">https://doi.org/10.5281/zenodo.5720198</a> or <a href="https://github.com/nick-garabedian/TriboDataFAIR-Ontology">https://github.com/nick-garabedian/TriboDataFAIR-Ontology</a> or <a href="https://fairsharing.org/3597">https://fairsharing.org/3597</a> - TriboDataFAIR Ontology</p> <p><a href="https://doi.org/10.5281/zenodo.5720218">https://doi.org/10.5281/zenodo.5720218</a> or <a href="https://github.com/nick-garabedian/SurfTheOWL">https://github.com/nick-garabedian/SurfTheOWL</a> - SurfTheOWL</p> <p><a href="https://kadi4mat.iam-cms.kit.edu/">https://kadi4mat.iam-cms.kit.edu/</a> - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook </p>
On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)
<p>This dataset includes the data to replicate the study for the paper <em>On-the-Fly Syntax Highlighting Using Neural Networks</em>. It can be reused for future research in the field. We also include the detailed results obtained by executing our approach.</p> <p>HLNN-Resources.zip includes the input data already formatted to be directly used with the shared source code.</p> <p>The paper is published in the proceeding of the <em>30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)</em>.</p>
CoHERE Work Package 5 Survey of free time activities amongst Latvian schoolchildren
<p>The quantitative survey «Youth and leisure time activities, informal education and cultural heritage» was carried out as part of Work Package 5 (Education, heritage and identities) of 'Critical Heritages: Performing and presenting identities in Europe' (https://research.ncl.ac.uk/cohere/researchstrands/). This Work Package develops best practices in the production and transmission of European heritages and identities within two sectors that face challenges in an age of immigration and globalization, namely education and cultural heritage production. It explores how European identity is shaped through formal and informal learning situations both in and outside the classroom with the purpose of enhancing school curricula and informal learning at heritage sites by integrating innovative technologies and including multicultural perspectives.</p> <p>The target group: youth (age 16 to 19) from secondary schools and professional education schools in Latvia </p> <p>Sample size: 1047. Time period: December 2017 – March 2018.</p> <p>Method: a self-administered questionnaire.</p> <p>The aim of the survey is to examine how cultural heritage shape different identities in Europe, representing ideas of place, history, traditions and sense of belonging.</p> <p>Tasks of the survey:<br> 1) to get information about leisure time activities of the youth and their participation in different informal education activities;<br> 2) to examine youth opinion about the role of cultural heritage and their involvement in safeguarding cultural heritage;<br> 3) to analyse the role of cultural heritage in formation of local, national and European identities of the young people.</p>
CoHERE Work Package 3 Survey of Inhabitants of Baltic Countries on Song and Dance Celebrations
<p>Part of Work Package 3 for the 'Critical Heritages' research project ( https://research.ncl.ac.uk/cohere/researchstrands/#WP3%20Cultural%20forms%20and%20expressions%20of%20identity%20in%20Europe ).</p> <p>One of the key case studies in CoHERE Work Package 3 has been the Song and Dance Celebration tradition in the Baltic states (included in the UNESCO list as a masterpiece of the oral and intangible heritage of humanity in 2003). The case study reveals several aspects of this festival: cultural, economic, social dimensions and governance. Through examining different aspects of this festival tradition and everyday practices it responds to several objectives of the WP3. Being a key social and cultural event in three Baltic countries, it provides a ground for debates on how performative practices and festivals can contribute to identity construction and transformation, developing sense of belonging, serve as platform for where heritage practices of different social groups can meet.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Dataset for "Study of Rapid Capacity Fade in Prismatic Li-ion Cells with Flexible Packaging"
<p>Prismatic lithium-ion batteries (LIBs) are considered promising electric energy sources in electromobility applications due to their cell to pack density. However, their sensitivity to external and internal influences, and reduced durability lead to inflation risk and potential explosions throughout their lifecycle. These critical processes are strongly influenced by the inner construction of the cell, especially concerning the coating and mechanical fixation. This study subjects a commercially available prismatic LIB cell to comprehensive, correlative analysis employing various imaging techniques. The inner structure of the entire cell is visualized non-destructively by X-ray computed tomography (CT), enabling the identification of critical design flaws prior to electrochemical cycling. Electrochemical cycling simulates the battery lifecycle, and the cell is subsequently disassembled in the fully charged state. The usage of the inert-gas transfer system allowed the preparation of Broad Ion Beam (BIB) electrodes cross-sections in a fully native state and for the first time to observe the tearing of graphite particles due to over-lithiation. Established region labeling system allowed to use CT and scanning electron microscopy (SEM) correlatively to identify critical regions. After 100 cycles, a 40% capacity loss was observed and event diagram describing deagradation mechanisms, related both to the cell design and to the processes occurring at high load, was created.</p>
Data Package for the 2022 Great Lakes Winter Grab
WARNINGS: 1. For Ice Thickness data, please use data in "WinterGrab_snow_ice_properties" file instead of data in Table 2 of the manuscript published in Limnology and Oceanography Letters! 2: In "WinterGrab_phytoplankton_abundance_McKay", EC1 has two sets of data records because it was sampled both on 2/28 and 3/10, both records are included in this data. --- The data package contains the results from a multi-institutional winter limnology sampling campaign on the Laurentian Great Lakes. Researchers from 19 institutions sampled 49 locations in all five of the Great Lakes over a period of 24 days in February-March 2022. This dataset contains information on diverse physical, chemical, and biological parameters. Great Lakes Winter Grab ArcGIS Storymap showing all locations of sampling sites and select photos: https://storymaps.arcgis.com/stories/8ff1c332dd944ba9a744dc0e0fc18906
Data package supporting manuscript "Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes"
This repository contains the complete data synthesis and analysis pipeline for a global meta-analysis on density-dependent mortality in reef fishes. We estimated mortality parameters (α and β) from >30 ecological studies and explored how ecological traits, experimental methods, and phylogenetic history explain variation in density dependence. It comprises eight data tables in csv format, three .tre files for phylogenetic trees (see method document for data sources), and the zipped code folder (including 12 R scripts) to ensure transparent, end-to-end reproducibility of data processing, analysis, and visualization. This package supports the manuscript “Widespread Heterogeneity in Density-Dependent Mortality of Nearshore Fishes” by Stier & Osenberg (Ecology Letters).
Replication package of "Search-based Crash Reproduction using Behavioral Model Seeding"
<p>Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the<br> search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both<br> the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 122 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency.</p>
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
S49 | CPPDBLISTB | Database of Chemicals possibly (List B) associated with Plastic Packaging (CPPdb)
<p>This is the collection associated with list S49 CPPDBLISTB on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S49 | CPPDBLISTB | <strong>Database of Chemicals associated with Plastic Packaging (CPPdb)</strong></p> <p>A database of chemicals likely (List A, 903 - in another upload) and possibly (List B, 3353 - this upload) associated with plastic packaging, with hazard data, from Groh et al 2019 DOI: <a href="https://doi.org/10.1016/j.scitotenv.2018.10.015">10.1016/j.scitotenv.2018.10.015</a>. Mapped to structures by CAS/Name by K. Groh & E. Schymanski. 2025: added new CSV file with duplicate headers renamed. </p> <p>Latest version of original data (last update Oct 2018): DOI: <a href="http://doi.org/10.5281/zenodo.1287773">10.5281/zenodo.1287773</a></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>
Reproduction package for the paper "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"
<p>Research Data Management package for "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"</p> <p>Authors: Sam Geen & Alex de Koter</p> <p>Status: Accepted by MNRAS<br> This package aims to provide a full data reproduction pipeline. Please see Readme.md for more information.</p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
On the Effectiveness of Transfer Learning for Code Search - Replication Package
<p>This repository represents the replication package for the paper <em>On the Effectiveness of Transfer Learning for Code Search</em>.</p> <p>The paper is published in the journal <em>IEEE Transactions on Software Engineering (TSE)</em>.</p> <p>In this replication package, we provide all the data and scripts we used in our study.</p>
Packaging Industry Anomaly DEtection (PIADE) Dataset
<p>PIADE dataset contains data from five industrial packaging machines:</p> <ul> <li>Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00</li> <li>Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00</li> <li>Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00</li> <li>Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00</li> </ul> <p>## Raw Data</p> <p>Each row represents a production interval, with the following schema:</p> <ul> <li>interval_start: start of the production interval </li> <li>equipment_ID: equipment identifier </li> <li>alarm: alarm code of the active stop reason, if it occurred </li> <li>type: idle, production, downtime, performance_loss or scheduled_downtime </li> <li>start: start of the production interval </li> <li>end: end of the production interval </li> <li>elapsed: duration of the production interval </li> <li>pi: input packages </li> <li>po: output packages </li> <li>speed: speed (packages per hour)</li> </ul> <p>There are 133 different types of alerts, and 429394 rows.<br> </p> <p>## Sequences (1h) data</p> <p>For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows:</p> <ul> <li>'equipment_ID': machine identifier</li> <li>'#changes': changes in machine state</li> <li>'%downtime': time spent in 'downtime' state</li> <li>'%idle': time spent in 'idle' state</li> <li>'%performance_loss': time spent in 'performance loss' state</li> <li>'%production': time spent in production</li> <li>'%scheduled_downtime': time spent in scheduled downtime</li> <li>'count_sum': sum of all alarm occurrences</li> <li>'A_<XXX>': counter of alarm <XXX> occurrences</li> <li>'<state1>/<state2>': number of transitions from <state1> to <state2></li> </ul> <p> </p>
Drug Interaction Study Data from the Drug Approval Package for Epidiolex (Cannabidiol)
<p>Data from the drug interaction studies reported in the drug approval package for Epidiolex (cannabidil), U.S. Food and Drug Administration: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/210365Orig1s000TOC.cfm.</p> <p>The data was manually extracted by a trained pharmacist from the PDF documents uploaded to drugs@fda for Epidolex drug approval package. The data extraction was reviewed for quality by an expert in pharamacology and natural product-drug interactions.</p>
Frictionless Tabular Data Package for GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data was extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>a supplementary material table available as a pdf from "Biosynthesis of monoterpene scent compounds in roses" by Magnard et al, Science 03 Jul 2015 identified by the following doi: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </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.