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2,984 results for “Raw data”
GATEMAN project -- D8.2 -- Field experiments raw data
<p>This dataset contains a .zip file (RawData.zip) with some of the raw files recorded during the open-field experimentation campaign in the frame of the GATEMAN project. These files are grouped for each type of validation scenario defined. The scenarios are separated in jamming and spoofing cases (JAM:Jamming, SPO:Spoofing) and classified according to the zone (Z1: Zone 1, Z2: Zone 2) and the vehicle motion (S:Static, D:Dynamic).</p> <p>Also the reference document of the deliverable D8.2 is included here.</p> <p><strong>Dataset Structure:</strong></p> <p>├── <strong>Jamming</strong><br> ├── OF-JAM-Z2-D-Test 11<br> └── Test_11.bin<br> ├── OF-JAM-Z2-D-Test 12<br> └── Test_12.bin</p> <p> ├--─ OF-JAM-Z2-D-Test 13<br> └── Test_13.bin<br> ├── GNURadio<br> └── SingleTone.grc</p> <p>├── <strong>Spoofing</strong><br> ├── OF-SPO-Z2-S-Test 1<br> └── Master_Test1.sbf<br> └── Slave_Test1.sbf</p> <p> ├── OF-SPO-Z2-S-Test 5<br> └── Master_Test5.sbf<br> └── Slave_Test5.sbf</p> <p> ├── OF-SPO-Z2-D-Test 2<br> └── Master_Test2.sbf<br> └── Slave_Test2.sbf</p> <p> ├── OF-SPO-Z2-D-Test 4<br> └── Master_Test4.sbf<br> └── Slave_Test4.sbf</p> <p> ├── OF-SPO-Z2-D-Test 6<br> └── Master_Test6.sbf<br> └── Slave_Test6.sbf</p> <p> ├── OF-SPO-Z1-D-Test 29<br> └── Master_Test29.sbf<br> └── Slave_Test29.sbf</p> <p> ├── GNURadio<br> └── Scenario1_decimated.iq<br> └── PreProcessing.grc<br> └── Signal_to_Usrp.grc</p> <p> </p>
Raw metagenomic data from sweep net samples collected in 2016 as part of the Slikok Creek Watershed Biotic Inventory
<p>We set out to inventory vascular plants, bryophytes, lichens, birds, arthropods, and earthworms on a grid of sites in the portion of Slikok Creek watershed that is on the Kenai National Wildlife Refuge, Kenai Peninsula, Alaska. Occurrence data, images, and field data sheets from this project are available via an <a href="https://arctosdb.org/">Arctos</a> project page at <a href="http://arctos.database.museum/project/10002227">http://arctos.database.museum/project/10002227</a>.</p> <p>This dataset includes the raw FASTQ files from metagenomic processing and associated collection data. Of the 160 sweep net samples collected, 125 were selected for High Throughput Sequencing and shipped to RTL Genomics (<a href="http://rtlgenomics.com">http://rtlgenomics.com</a>) for extraction and sequencing steps. Sequencing was performed on an Illumina MiSeq platform and reads were processed using RTL Genomics’ standard methods with the mlCOIlintF/HCO2198 primer set of Leray et al. (2013), yielding a 313 bp region of the COI gene.</p> <p>Collection data are included in the file <code>ArctosData_43C6167EB1.csv</code> downloaded from Arctos. Extraction methods and sequencing methods provided by RTL Genomics are included in the files <code>Bowser 4869.pdf</code> and <code>Illumina MiSeq Two-Step Method 454 profile only.docx</code>. Primers used are provided in the file <code>Bowser_4869M.txt</code>. The archive <code>FASTQ.zip</code> contains all of the resulting FASTQ files.</p> <p>These sequence data have also been been published to GenBank's Sequence Read Archive in accessions <a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454582">SRR10454582</a>–<a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454706">SRR10454706</a> under BioProject <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA427721">PRJNA427721</a>.</p>
Raw data for analysis of archaeological collections using scoring
<p>The file includes data in the form of attributes identifying the cultural or natural origin of archaeological collections composed of flakes and blades made of flint. The collections attributed to the Lower and Middle Palaeolithic in Poland and Germany. The last collection consists of experimentally produced samples. These collections are held at the Muzeum Śląska Opolskiego in Opole, University of Wrocław, University of Silesia in Katowice, Sosnowiec and Landesmuseum für Vorgeschichte in Halle (Saale). The collected data was used to improve the method hitherto applied to distinguish sets composed of artefacts and pseudo-artefacts using so-called scoring. This work was financially supported by the National Science Centre,<br>Poland (Grant 2020/39/B/HS3/02277).</p>
Raw NanoLuc and NanoBiT Data from "Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay"
<p>This dataset includes raw NanoLuc and NanoBiT data supporting the manuscript titled 'Accurate and Sensitive Interactome Profiling Using a Quantitative Protein-Fragment Complementation Assay'.</p>
QMC Raw Data for "Friedel oscillations in one-dimensional 4He"
<p>Raw path integral quantum Monte Carlo data associted with "Friedel oscillations in one-dimensional 4He". Connected github repository with associated analysis scripts can be found at <a title="Github repo" href="https://github.com/DelMaestroGroup/papers-code-HourglassNanopores">https://github.com/DelMaestroGroup/papers-code-HourglassNanopores.</a> Path integral software is available: <a title="PIMC" href="https://github.com/DelMaestroGroup/pimc">https://github.com/DelMaestroGroup/pimc</a></p> <h2><strong>Contents</strong></h2> <p><code>dR_eq_{δR}.tar.gz</code> includes raw QMC data where <code>{δR} = {0.0,2.0,3.0.4.0} Å</code> is the perturbed radius of the hourglass. All simulations include a perturbation with width <code>w = 3.0 Å</code>. Temperature is fixed at <code>T = 2.0 K</code>. </p> <p>Each <code>.tar.gz</code> contains <code>log</code> and <code>estimator</code> files for the simulations described in the associated paper. </p> <p><code>U_hourglass.npz</code>: generated xy mesh for potential plotting. </p> <p><code>gce-position-dR_eq_{δR}.npz</code>: 3D local densities inside the nanopore</p> <p> </p> <h3>Notes</h3> <p>v3: This version contains <code>gce-position-dR_eq_{δR}.npz</code> which used to have to be regenerated from raw QMC data.</p> <p>v2: This version contains an updated <code>dR_eq_2.0.tar.gz</code> (the one in v1 was corrupted).</p> <p> </p>
Raw Data for "Vanadium incorporation in ferrite nanoparticles serves as electron buffer and anisotropy tuner in catalytic and hyperthermia applications"
<p>Raw data for VxFe2-XO4 magnetic nanoparticles: characterization,magnetic hyperthermia experiments in polyacrylamide gels and EPR reactive oxygen species quantification after exposure to H2O2.</p>
Supplementary data to Challenges and Opportunities for the Recovery of Critical Raw Materials from Electronic Waste
<p>Content of the excell file:</p> <p>Table S1: Relationship between UNU-keys and MINCOTUR codes</p> <p>Table S2: UNU-Key composition and alpha and beta values for Weibull distributions</p> <p>Table S3: Metal prices used in this study.</p>
Raw data for Infrastructure and Awareness Landscape Analysis in sub-Saharan Africa
<p>Persistent identifiers that are well-connected are essential for enhancing research, researchers, and research institutions. The comprehensive raw data shared on PIDs infrastructure and awareness landscape analysis in sub-Saharan Africa is taken from service providers and organizations, including Open DOAR, the Registry of Open Access Repositories (ROAR), the Registry of Research Data Repositories (Re3data), UNESCO, Lyrasis (Dspace), Dataverse, Open Journal System (OJS), among others. The data shared here was collected in August 2023. The data shared are secondary data, and the position is strictly based on the primary source data author. The data is restricted to what is available on the internet and does not include locally hosted offline data.</p> <p>Further analysis of the raw data suggested some salient implications for PIDs awareness in the region. Variations were observed across the various data sources, while some interesting correlations emerged from the collected data. There are countries with some PIDs infrastructure, while others are yet to establish a visible presence in PIDs infrastructure. The visibility of PIDs is somewhat related to the awareness level as well as the policy established on open access in the represented countries across the region.</p>
EXPLO. Sovjan 2021. Radiocarbon raw data from tree-rings
<p>Raw radiocarbon measurements from the tree-rings sampled from wooden elements from the archaeological site of Sovjan, Albania, measured in 2020 and presented in "<em>The Early Bronze Age dendrochronology of Sovjan (Albania): A first tree-ring sequence of the 24th – 22nd c. BC for the southwestern Balkans</em>", Maczkowski et al., 2021, DOI: <a href="http://dx.doi.org/10.1016/j.dendro.2021.125811">10.1016/j.dendro.2021.125811</a></p>
7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.</p>
Raw data of peacock butterflies
<p>We provide the raw peacock butterfly data. </p> <p>The id from waarnemingen.be, scientific name (always Aglais io), date, time (when available), number of individuals, lifestage, the coordinates x and y in Lambert 1972 (EPSG:<em>31370</em> Projected coordinate system for Belgium) and lon, lat in WGS84, the municipality in in Belgium (due to a mistake, this column is named community in the data file), the link to the original observation and the number of photo's included in this observation. </p> <p> </p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
PQRI Elemental Impurity Interlaboratory Study Raw Data
<p>The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters <232> and <233>, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations.</p> <p>The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.</p>
Raw data and heatmaps of VLP deposition modeling
<p>Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofstätter N., Hofer S., Duschl A., and Himly M.<br> Children’s privilege in COVID-19: The protective role of the juvenile lung morphometry and ventilatory pattern on airborne SARS-CoV-2 transmission and severe pulmonary disease (2021). <em>Biomedicines </em>9(10):1414.</a> DOI: <a href="https://doi.org/10.3390/biomedicines9101414">https://doi.org/10.3390/biomedicines9101414</a></p> <p>1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon nose breathing</p> <p>2. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon mouth breathing</p> <p>3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon nose breathing</p> <p>4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon mouth breathing</p> <p>5. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon nose breathing</p> <p>6. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon mouth breathing</p> <p>7. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon nose breathing</p> <p>8. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon mouth breathing</p>
Raw data from Qin et al. (2018) "Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles"
<p>Raw hydrogen concentration vs. time data from Qin, H., X. Guan, J. Z. Bandstra, R. L. Johnson, and P. G. Tratnyek (2018) “Modeling the kinetics of hydrogen formation by zerovalent iron: Effects of sulfidation on micro- and nano-scale particles” Environ. Sci. Technol. 52(23): 13887-13896. [10.1021/acs.est.8b04436]</p> <p>This manuscript reports a large set of new concentration vs. time data for dihydrogen (H2) produced by corrosion of granular zerovalent iron (i.e., the hydrogen evolution reaction, HER) in aqueous media relevant to groundwater remediation. Four alternative kinetic models are evaluated by fitting the data using global non-linear regression. Details are given in the main text and supporting information of the (open access) manuscript. </p> <p>The data provided here are in two formats: (i) a .csv file that contains only data and labels, and (ii) a .pxp file that includes the data and graphs (without fits) in the same layout as figures in the original manuscript. The .pxp file was prepared with Igor Pro 8.02 (https://www.wavemetrics.com).</p>
An optimized buffer for repeatable Multicolor STORM Raw Data
<p>Raw microcopy data used to generate the figures in the paper "An optimized buffer for repeatable Multicolor STORM"</p> <p>Camera: Orca Fusion binning 2x2 readout speed 1</p> <p>Objective 100x/1.3 (Olympus) -> Effective pixel size 130x130 nm</p> <p>Expected Conversion factor according to spec sheet: 0.21 electrons/count</p> <p> </p> <p> </p> <p> </p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
Raw Synthetic Real-World Data
<p>Real World Data from a simulator for 1,000 people belonging to any of four behavioural groups: athletic, normal, unfit and feeble, simulated over 2 years and 3 months.</p> <p>The dataset is organised into 4 CSV files:</p> <ul> <li>aggregations.csv contains the daily physiological data, including their short- & long-term averages and their trends. Every row corresponds to a day for a person.</li> <li>answers.csv contains the answers to questions. Each row corresponds to a question answered.</li> <li>activity_summaries.csv contains summaries of the detected activities. Each row corresponds to an activity someone performed.</li> <li>participants_features.csv contains the demographic info of each simulated person. This is static info, every row corresponding to one person. An ML engineer can combine information from any of these files to setup model learning datasets. This is done in the “Processed Synthetic RWD for binary modelling” and the “Processed Synthetic RWD for tristate modelling” datasets that are also shared here.</li> </ul>
2D Cardiac black-blood TSE MR raw data
<p>Raw data in ismrmrd format obtained with a 2D black-blood TSE sequence on a 3T Siemens Verio scanner in three different orientations. This data is used as test data for the comparison of different open-source image reconstruction packages provided here: <a href="https://github.com/ckolbPTB/OpenSourceMrRecon">OpenSourceMrRecon</a></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.