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Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</p>
Endurance swimming performance and physiology of juvenile Green Sturgeon (Acipenser medirostris) at different temperatures, CA, 2022
This dataset provides information on the endurance swimming performance and physiological responses of juvenile Green Sturgeon (Acipenser medirostris), reared and tested at the University of California, Davis, in 2022. Fish were acclimated to two temperature treatments (13°C and 18°C) for 14 days prior to swimming trials. Endurance tests were conducted at 47–53 days post-hatch (DPH) in modified swim tunnels at fixed water velocities (25–55 cm s⁻¹) to measure time-to-fatigue (End.min), station-holding behavior (Station_holding), and swimming type (Swim.type). Fish morphometrics (e.g., weight, fork length, total length) were recorded before trials. Post-swim physiological analyses included whole-body measurements of cortisol, glucose, lactate, and protein. Tissue homogenates were processed to determine concentrations normalized to fish weight (e.g., Cortisol_ng_g, Glucose_ug_g, Lactate_ug_g). Standard curves showed high assay linearity (R² > 0.98) and low variability (CV < 10%). This dataset contributes to understanding sturgeon endurance and physiological stress under different environmental conditions, providing insights into their resilience to temperature and flow changes relevant to river management and conservation efforts. Variables include: species, developmental stage (DPH), rearing and trial conditions (tank, temperature, velocity), fish morphometrics (weight, fork length, total length), and physiological metrics (cortisol, protein, glucose, and lactate).
Dataset of "Preparation of novel lithiated high-entropy spinel type oxyhalides and their electrochemical performance in Li-ion batteries "
<p>Electrochemical measurements carried out using the 2032-coin cells with the Li-metal anode have shown voltammetric charge capacities of 450, 694, and 593 mAh g-1 for HEOFe, LiHEOFeCl, and LiHEOFeF, respectively.<br>Galvanostatic chronopotentiometry at 1 C rate confirmed high initial charge capacities for all the samples but galvanostatic curves exhibited a capacity decay over 100 charging/discharging cycles. Raman spectroelectrochemistry measured on the LiHEOFeF sample proved the reversibility of the electrochemical process for initial charging/discharging cycles. Electrochemical impedance spectroscopy revealed the lowest initial charge transfer resistance for LiHEOFeCl and its gradual decrease both for LiHEOFeCl and LiHEOFeF during galvanostatic cycling, whereas the charge transfer resistance of HEOFe slightly increases over 100 galvanostatic cycles due to different mechanism of the electrochemical reduction. </p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities
<p>Example <strong>Microscopy Metadata </strong>(Microscope.JSON and Settings.JSON)<strong> files </strong>produced using<strong> <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> </strong>to document the <strong>Hardware Specifications</strong> of example Microscopes and the <strong>Image Acquisition Settings</strong> utilized to acquire example images as listed in the table below.</p> <blockquote> <p>For each facility, the dataset contains two JSON files:</p> <ol> <li><strong>Microscope.JSON file</strong> (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)</li> <li><strong>Settings.JSON file</strong> (indicated with the name of the image and with the _AS suffix)</li> </ol> </blockquote> <p><strong>Micro-Meta App was</strong> developed as part of a <strong>global community initiative</strong> including the <a href="http://www.4dnucleome.org/"><strong>4D Nucleome (4DN)</strong> </a>Imaging Working Group, <strong>BioImaging North America (BINA)</strong> <a href="https://www.bioimagingna.org/qc-dm-wg">Quality Control and Data Management Working Group</a>, and <strong>QUAlity and REProducibility for Instrument and Images in Light Microscopy</strong> (<a href="https://quarep.org/"><strong>QUAREP-LiMi</strong></a>), to extend the <strong>Open Microscopy Environment (OME)</strong> <a href="https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome.html">data model</a>.</p> <blockquote> <p>The works of this <strong>global community effort</strong> resulted in multiple publications featured on a recent <strong>Nature Methods FOCUS ISSUE </strong>dedicated to <a href="https://www.nature.com/collections/djiciihhjh">Reporting and reproducibility in microscopy</a>.</p> </blockquote> <blockquote> <p><strong>Learn More!</strong> For a thorough description of <strong>Micro-Meta App</strong> consult our recent <a href="https://doi.org/10.1038/s41592-021-01315-z">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.05.31.446382">BioRxiv.org</a> publications!</p> </blockquote> <p> </p> <table> <tbody> <tr> <td><strong>Nr.</strong></td> <td><strong>Manufacturer</strong></td> <td><strong>Model</strong></td> <td><strong>Tier</strong></td> <td><strong>Εxperiment Type</strong></td> <td><strong>Facility Name</strong></td> <td><strong>Department and Institution</strong></td> <td><strong>URL</strong></td> <td><strong>References</strong></td> </tr> <tr> <td>1</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with LSM 710 scan head)</strong></td> <td>1</td> <td>3D visualization of superhydrophobic polymer-nanoparticles</td> <td>Centre for Cell Imaging (CCI)</td> <td>University of Liverpool</td> <td>https://cci.liv.ac.uk/equipment_710.html</td> <td>Upton et al., 2020</td> </tr> <tr> <td>2</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer (Axiovert 200M)</strong></td> <td>2</td> <td>Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP</td> <td>Advanced BioImaging Facility (ABIF).</td> <td>McGill University</td> <td>https://www.mcgill.ca/abif/equipment/axiovert-1</td> <td>Kiepas et al., 2020</td> </tr> <tr> <td>3</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with Spinning Disk)</strong></td> <td>2</td> <td>Immunofluorescence imaging of cryosection of Mouse kidney</td> <td>Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)</td> <td>Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal</td> <td>https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)</td> <td>Pilliod et al., 2020</td> </tr> <tr> <td>4</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Imager Z2 (with Apotome)</strong></td> <td>2</td> <td>Immunofluorescence imaging of mitotic division in Hela cells using </td> <td>Bioimaging Unit</td> <td>Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/</td> <td>Watson et al., 2020</td> </tr> <tr> <td>5</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1</strong></td> <td>2</td> <td>Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.</td> <td>Life Imaging Center (LIC)</td> <td>Centre for Integrative Signalling Analysis (CISA), University of Freiburg</td> <td>https://miap.eu/equipments/sd-i-abl/</td> <td>Hannibal et al., 2020</td> </tr> <tr> <td>6</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI6000B</strong></td> <td>2</td> <td>3D immunofluorescence imaging rhinovirus infected macrophages </td> <td>IMAG'IC Confocal Microscopy Facility</td> <td>Institut Cochin, CNRS, INSERM, Université de Paris</td> <td>https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view</td> <td>Jubrail et al., 2020</td> </tr> <tr> <td>7</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DM5500B</strong></td> <td>2</td> <td>Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks</td> <td>Bioimaging Unit</td> <td>Edwardson Building on the Campus for Ageing and Vitality, Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview</td> <td>da Silva et al., 2019; Nelson et al., 2012<br> </td> </tr> <tr> <td>8</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI8-CS (with TCS SP8 STED 3X)</strong></td> <td>2</td> <td>Live-cell imaging of N. benthamiana leaves cells-derived protoplasts</td> <td>Center for Advanced Imaging (CAi)</td> <td>School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf</td> <td>https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x</td> <td>Singer et al., 2017; Hänsch et al., 2020</td> </tr> <tr> <td>9</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti</strong></td> <td>2</td> <td>Immunofluorescence analysis of the cytoskeleton structure in COS cells</td> <td>Advanced Imaging Center (AIC)</td> <td>Janelia Research Campus, Howard Hughes Medical Institute</td> <td>https://www.janelia.org/support-team/light-microscopy/equipment</td> <td>Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020</td> </tr> <tr> <td>10</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti-E (HCA)</strong></td> <td>2</td> <td>Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies</td> <td>Light Microscopy Facility (IALS-LIF)</td> <td>Institute for Applied Life Sciences, University of Massachusetts at Amherst</td> <td>https://www.umass.edu/ials/light-microscopy</td> <td>Fernandez et al., 2020</td> </tr> <tr> <td>11</td> <td><strong>Nikon Instruments/Coleman laboratory (customized)</strong></td> <td><strong>TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti</strong></td> <td>2</td> <td>Single-particle tracking of Halo-tagged PCNA in Lox cells</td> <td>Coleman laboratory</td> <td>Anatomy and Structural Biology Department, The Albert Einstein College of Medicine</td> <td>https://einsteinmed.org/faculty/12252/robert-coleman/</td> <td>Drosopoulos et al., 2020</td> </tr> <tr> <td>12</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti (with Andor Dragon Fly Spinning Disk)</strong></td> <td>2</td> <td>Investigation of the 3D structure of cerebral organoids</td> <td>Montpellier Resources Imagerie</td> <td>Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier</td> <td>https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html</td> <td>Ayala-Nunez et al., 2019</td> </tr> <tr> <td>13</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium </td> <td>Neuroscience Center Microscopy Core</td> <td>Neuroscience Center, University of North Carolina</td> <td>https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/</td> <td>Aghajanian et al., 2021</td> </tr> <tr> <td>14</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Live-cell imaging of bacterial cells expressing GFP-PopZ</td> <td>Microscopy Resources on the North Quad (MicRoN)</td> <td>Harvard Medical School </td> <td>https://micron.hms.harvard.edu/</td> <td>Lim and Bernhardt 2019; Lim et al., 2019</td> </tr> <tr> <td>15</td> <td><strong>Olympus/Biomedical Imaging Group (customized)</strong></td> <td><strong>TIRF Epifluorescence Structured light Microscope (TESM)/IX71</strong></td> <td>3</td> <td>3D distribution of HIV-1 in the nucleus of human cells</td> <td>Biomedical Imaging Group</td> <td>Program in Molecular Medicine, University of Massachusetts Medical School</td> <td>https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope</td> <td>Navaroli et al., 2012</td> </tr> <tr> <td>16</td> <td><strong>Olympus/Computer Vision Laboratory (customized)</strong></td> <td><strong>3D BrightField Scanner/IX71</strong></td> <td>3</td> <td>Transmitted light brightfield visualization of swimming spermatocytes</td> <td>Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology</td> <td>Universidad Nacional Autonoma de Mexico (UNAM)</td> <td>https://lnma.unam.mx/wp/</td> <td>Pimentel et al., 2012; Silva-Villalobos et al., 2014</td> </tr> </tbody> </table> <p><strong>Getting started</strong></p> <p>Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:</p> <ol> <li><a href="https://vimeo.com/562022222">Video 1</a></li> <li><a href="https://vimeo.com/562022281">Video 2</a></li> </ol> <p><strong>More information</strong></p> <blockquote> <p>For full information on how to use Micro-Meta App please utilize the following resources:</p> <ol> <li>Micro-Meta App <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">website</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/index.html">Full documentation</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/intro/installation.html">Installation</a> instructions</li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/index.html#step-by-step-instructions">Step-by-Step Instructions</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/VideoTutorials.html#micro-meta-app-video-tutorials">Tutorial Videos</a></li> </ol> </blockquote> <p><strong>Background</strong></p> <p>If you want to learn more about the importance of <strong>metadata and quality contro</strong>l to ensure full <strong>reproducibility, quality and scientific value</strong> in light microscopy, please take a look at our recent publications describing the development of community-driven light <strong>4DN-BINA-OME Microscopy Metadata</strong> specifications <a href="https://doi.org/10.1038/s41592-021-01327-9">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.04.25.441198">BioRxiv.org</a> and our <a href="https://arxiv.org/abs/1910.11370">overview manuscript</a> entitled <strong>A perspective on Microscopy Metadata: data provenance and quality control</strong>.</p> <p> </p> <p> </p>
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Benchmarking on Microservices Configurations and the Impact on the Performance in Cloud Native Environments
<p><strong>The peer reviewed publication for this dataset has been published in LCN 2022, 47th Annual IEEE Conference on Local Computer Networks. Please cite this paper when referring to the dataset: https://www.eurecom.fr/publication/6971.</strong></p> <p>Cloud-native and containerization have changed the way to develop and deploy applications. Cloud-native rethinks the application architecture by embracing a microservice approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the needed computing resources to run their workload in terms of the amount of CPU and memory limit. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will have an impact not only on the service performances but also on the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct an experimental study aiming to detect if a tenant's configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results are presented in the accepted IEEE LCN paper (https://www.eurecom.fr/publication/6971) and are shared in this dataset.</p> <p>The datasets are collected for 3 types of applications: Web servers written in python and Golang, RabbitMQ data broker and the OpenAirInterface 5G Core network function AMF (Access and Mobility Management Function).</p> <p><br> </p> <p><strong>Web Servers:</strong></p> <p><strong>files: </strong>golang-web-server-performance.csv, python-web-server-performance.csv</p> <p>We used Golang and Python-based web servers for the test. Each request to the web server returns a video of a size 43 MB. For testing we used ApacheBench, a command-line program used for benchmarking HTTP web servers. ApacheBench allows parallel requests from multiple clients. For each web server instance we send a number of requests ranging from 100 to 1000 and a concurrency level between 1 and 100, representing the number of parallel clients performing the requests.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of requests sent to the container.</p> <p><strong>c:</strong> the concurrency level in the requests.</p> <p><strong>lat50:</strong> the least response time for the best 50% requests in microseconds.</p> <p><strong>lat66:</strong> the least response time for the best 66% requests in microseconds.</p> <p><strong>lat75:</strong> the least response time for the best 75% requests in microseconds.</p> <p><strong>lat80:</strong> the least response time for the best 80% requests in microseconds.</p> <p><strong>lat90:</strong> the least response time for the best 90% requests in microseconds.</p> <p><strong>lat95:</strong> the least response time for the best 95% requests in microseconds.</p> <p><strong>lat98:</strong> the least response time for the best 98% requests in microseconds.</p> <p><strong>lat99:</strong> the least response time for the best 99% requests in microseconds.</p> <p><strong>lat100:</strong> the least response time in microseconds.</p> <p> </p> <p><strong>5G Core network’s AMF:</strong></p> <p><strong>file: </strong>amf-performance.csv</p> <p>For testing we use my5G-RANTester, a tool for emulating control and data planes of the UE and gNB (5G base station). The number of simultaneous registration requests that are sent to each instance of the AMF varies between 10 and 400.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of parallel registration requests sent to the AMF.</p> <p><strong>mean:</strong> the mean registration time for all the registration requests in microseconds.</p> <p><strong>lat50:</strong> the median registration time for registration requests in microseconds.</p> <p><strong>lat75: </strong>the least registration time for the best 75% registration requests in microseconds.</p> <p><strong>lat80:</strong> the least registration time for the best 80% registration requests in microseconds.</p> <p><strong>lat90:</strong> the least registration time for the best 90% registration requests in microseconds.</p> <p><strong>lat95:</strong> the least registration time for the best 95% registration requests in microseconds.</p> <p><strong>lat98:</strong> the least registration time for the best 98% registration requests in microseconds.</p> <p><strong>lat99:</strong> the least registration time for the best 99% registration requests in microseconds.</p> <p><strong>lat100:</strong> the least registration time in microseconds.</p> <p> </p> <p><strong>RabbitMQ data broker:</strong></p> <p><strong>file: </strong>rabbitmq-performance.csv</p> <p>For testing we used RabbitMQ PerfTest which is a throughput testing tool that simulates basic workloads and provides the throughput and the time that a message takes to be consumed by a consumer. For each deployed RabbitMQ server we used a number of producers and consumers that ranges from 50 to 500. Each producer sends messages to the broker with a rate of 100 messages per second for a period of time of 90 seconds.</p> <p>The information available in the dataset are as follows:</p> <p><strong>time:</strong> timestamp of collection of metrics.</p> <p><strong>ram_limit:</strong> the memory allocated to the container in megabytes.</p> <p><strong>cpu_limit:</strong> the CPU allocated to the container.</p> <p><strong>ram_usage:</strong> the amount of memory used by the container at the time of the metrics collection in byte.</p> <p><strong>cpu_usage:</strong> the amount of CPU used by the container at the time of the metrics collection.</p> <p><strong>n:</strong> the number of producers sending messages to the RabbitMQ server.</p> <p><strong>Min:</strong> the minimum consumption time for the producer messages.</p> <p><strong>lat50:</strong> the median consumption time for the producer messages.</p> <p><strong>lat75:</strong> the least consumption time for the best 75% messages in microseconds.</p> <p><strong>lat95:</strong> the least consumption time for the best 95% messages in microseconds.</p> <p><strong>lat99:</strong> the least consumption time for the best 99% messages in microseconds.</p>
Data from: ChatGPT performance on radiation technologist and therapist entry to practice exams
<p>This dataset contains the data needed to reproduce all results and figures described in "ChatGPT performance on radiation technologist and therapist entry to practice exams".</p> <p>Details about the data collection can be found in the paper referenced below. Briefly, ChatGPT (GPT-4) was prompted with multiple choice questions from 4 practice exams provided by the Canadian Association of Medical Radiation Technologists (CAMRT). ChatGPT was promted with the questions from each exam 5 times between July 17 and August 13, 2023. Table 1, below, provides details about the dates for data collection.<br><br></p> <p><strong>Variable descriptions</strong></p> <ul> <li><code>question</code>: Question number, provided by CAMRT. Skipped question numbers indicate image-based questions that were excluded from the study.</li> <li><code>discipline</code>: Indicates the CAMRT exam discipline, abbreviated as follows <ul> <li>RAD: radiological technology</li> <li>MRI: magnetic resonance</li> <li>NUC: nuclear medicine</li> <li>RTT: radiation therapy</li> </ul> </li> <li><code>question_type</code>: Indicates the type of competency being assessed by the question (Knowledge, Application, or Critical thinking). Competency categories were assigned by CAMRT.</li> <li><code>corrrect_response</code>: The correct multiple choice response ("A", "B", "C", or "D"), assigned by CAMRT.</li> <li><code>attempt1-5</code>: ChatGPT's response to the multiple choice questions for attempts 1 through 5, indicated using the letters "A", "B", "C", or "D". In a few cases, ChatGPT did not provide a reference to a multiple choice response and "NA" is recorded in the dataset. </li> </ul> <p><em>Note: The long-form questions from CAMRT and answers provided by ChatGPT are not available as a part of this dataset.<br><br></em></p> <p><strong>Table 1</strong>: Dates for data collection</p> <table> <tbody> <tr> <td> </td> <td><strong>Attempt 1</strong></td> <td><strong>Attempt 2</strong></td> <td><strong>Attempt 3</strong></td> <td><strong>Attempt 4</strong></td> <td><strong>Attempt 5</strong></td> </tr> <tr> <td><strong>Radiological technology</strong></td> <td>2 Aug 2023</td> <td>2 Aug 2023</td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>11 Aug 2023</td> </tr> <tr> <td><strong>Magnetic resonance </strong></td> <td>17 Jul 2023</td> <td>18 Jul 2023</td> <td>18 Jul 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td><strong>Nuclear medicine</strong></td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td><strong>Radiation therapy</strong></td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>13 Aug 2023</td> <td>13 Aug 2023</td> </tr> </tbody> </table> <p> </p>
Dataset for "Impact of the flow-field distribution channel cross-section geometry on PEM fuel cell performance: stamped vs. milled channel"
<p>Experimental data comprises raw data from load curve characterisation of a PEM fuel cell used for the validation of the mathematical model. Model data comprise of space-dependent values of hydrogen and oxygen concentration, local current densities, gas pressures and gas velocities in the modelled cell. These data were used for the investigation of the effect of different geometric parameters of flow-field channels on the performance of a PEM fuel cell.</p>
Endurance swimming performance and physiology of juvenile sturgeon at different temperatures, 2022-2024
Endurance swimming trials were conducted on both Green Sturgeon (Acipenser medirostris) and White Sturgeon (Acipenser transmontanus) across multiple size classes to assess the effects of temperature and velocity on swimming performance. Fish were exposed to various water temperatures for at least 14 days and swam at fixed-water velocities (cm/s) in controlled swim tunnels. Data were collected on parameters such as species, size class, trial temperature, velocity, recovery time, time-to-fatigue, swim type classifications, and whether the trials were completed. Additional metadata included fish morphometrics such as fork length, total length, weight, and trial dates, enabling comparisons across species, size, and treatment conditions. Physiological responses were measured post-swimming to evaluate the impacts of endurance trials on the smallest size class of Green Sturgeon (5cm fork length): - In 2022, whole-body cortisol, glucose, and lactate concentrations were measured immediately following endurance trials (0 min recovery). - In 2023, recovery dynamics were incorporated, with physiological responses assessed at multiple time points (0 min, 15 min, 30 min, and 60 min post-trial). - In 2022 and 2023 the baseline physiological metrics (whole-body cortisol, glucose, and lactate concentrations) of control fish (not subjected to swimming trials) were measured across all temperatures, years, species, and size classes. This dataset provides information on sturgeon swimming performance under varied temperature conditions, as well as the associated physiological stress responses, offering valuable insights into their endurance capabilities and recovery processes. The findings can inform conservation strategies, habitat management, and aquaculture practices for these ecologically and economically important species.
Impact of Snowmelt Timing and Tree Proximity on Dutchman's Breeches Phenology and Performance in Mont Megantic National Park (Quebec, Canada; 2018-2019)
Data herein were collected in 2018 and 2019 in Mont Megantic National Park, Quebec, Canada, in a sugar maple-dominated temperate deciduous forest. Individuals of Dutchman's breeches (Dicentra cucullaria), a common understory spring ephemeral plant that is only active in the spring, were transplanted into a fully factorial experiment of snowmelt timing (early vs. late) and tree proximity (near vs. far) to determine the role of thaw circle formation in the local clustering of this species near canopy tree trunks. Plant phenology (emergence, senescence, and growing season length) and performance (stem abundance and leaf area) were tracked during two years of snow manipulation. Additionally, microclimate temperature data were collected in a subset of plots in 2018.
Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool
<p>This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu</p>
Experimental data for the motor learning study performed: "Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. The details of the study are described in [doi: 10.3389/fnins.2020.600059]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses are stored as a “csv” file. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Design of Multifunctional Composites: New Strategy to Save Energy and Improve Mechanical Performance
<p>dataset on </p> <p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering</p> <p>FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry,</p> <p>Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement,</p> <p>Transmission Electron Microscopy and Stress Strain</p>
UShER performance statistics, SARS-CoV-2 daily builds 2021-2023
<p>For each day from 2021-01-07 through 2023-08-01 on which the daily build update of the UShER tree of SARS-CoV-2 genomes completed, the number of new sequences added to the tree, the number of sequences in the updated tree, the number of parallel usher jobs (original usher through 2022-04-27, usher-sampled starting 2022-04-29), the number of CPU cores per usher job, and approximate runtime of the usher batch in hours are listed. The number of sequences in the updated tree is "n/a" for most dates prior to 2021-03-11 because before that point, daily updates were for the public-sequence-only tree and the comprehensive GISAID and public sequence tree was updated only occasionally. On and after 2021-03-11, the comprehensive tree and public tree were updated daily. The runtime figures are approximate because they are calculated by subtracting the file modification date of the VCF input to usher from the file modification date of the MAT output of usher. On most days, that was a good proxy for usher runtime, but occasionally there was a crash that required debugging and/or restart, and the "runtime" includes those delays.</p>
Test-bed PV system performance data
<p>The data is generated from the on-site data acquisition devices installed at the outdoor testing facilities of the Smart Energy Infrastructure | PHAETHON CoE. </p>
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Dataset to "Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo"
<p>This record contains pre-processed data of a ten-and-a-half-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasProcessed_YYYY-MM-DD.dat correspond to the prepared data into a form for data analysis and processing as presented in “Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo”, accepted for publication in journal energy and buildings (<a href="https://doi.org/10.1016/j.enbuild.2022.111896">https://doi.org/10.1016/j.enbuild.2022.111896</a>).</p> <p>Each file, format MeasProcessed _YYYY-MM-DD.dat, corresponds to the daily registered data monitored every minute.</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuration, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
Benchmark dataset for preprint: "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator"
<p>The benchmark files and scripts to reproduce the figures of the preprint "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator" ( https://arxiv.org/abs/2106.06752 )</p> <p>The benchmarks require a computer running Linux with Docker installed.</p> <p>Unpack the paper_experiments.zip file and follow the instructions in the README.md file to run the benchmarks and reproduce the figures.</p> <p> </p>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</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.