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2,984 results for “Raw data”
Example imaging mass cytometry raw data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the <strong>I</strong>ntegrated i<strong>MMU</strong>noprofiling of large adaptive <strong>CAN</strong>cer patient cohorts (IMMUcan) project (<a href="https://immucan.eu">immucan.eu</a>) using the Hyperion imaging system (<a href="https://www.fluidigm.com/products-services/instruments/hyperion">www.fluidigm.com/products-services/instruments/hyperion</a>). To get an overview on the technology and available analysis strategies, please visit <a href="https://bodenmillergroup.github.io/IMCWorkflow/">bodenmillergroup.github.io/IMCWorkflow</a>. The individual data files are described below:</p><ul><li><strong>Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip</strong>: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.</li><li><strong>compensation.zip</strong>: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: <a href="https://doi.org/10.1016/j.cels.2018.02.010">Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry</a></li><li><strong>panel.csv</strong>: This file contains metadata for each antibody/channel used in the experiment. The <i>full</i> column indicates which channel should be analysed. The <i>ilastik</i> column specifies which channels were used for ilastik pixel classification and the <i>deepcell</i> column indicates the channels used for deepcell segmentation.</li><li><strong>sample_metadata.csv</strong>: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).</li></ul>
Raw data to "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types"
<p>This collection of data is complementary to the publication "Series expansions in closed and open quantum many-body systems with multiple quasiparticle types", Lea Lenke, Andreas Schellenberger, Kai Phillip Schmidt, <a href="https://arxiv.org/abs/2302.01000">arXiv:2302.01000</a> (<a href="https://arxiv.org/abs/2302.01000">https://arxiv.org/abs/2302.01000</a>).</p> <p>It contains all data used for Figure 2 given in the file `Figure_2_complementary_data.yaml` and all needed data to recalculate the energies of the visualized modes in the files `Figure_2_coefficients_expectation_values.yaml` and `Figure_2_broad_signum_coefficients_expectation_values.yaml`.</p> <p>For the last two files, we used a program to calculate the coefficients. The source code for coefficient calculation is openly available under GitHub (<a href="https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator">https://github.com/FAU-kpslab/pcstpp_CoefficientGenerator</a>) including configuration files to reproduce the coefficients given here.</p> <p>All files are self-consistent, for further information we recommend the comments directly in the files.</p> <p>For further details on the used method pcst<sup>++ </sup>and discussion of the results we refer to the linked publication.</p> <p>If any question may arise, you are highly welcome to contact us (see e.g. contact information on the publication).</p>
Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''
<p>Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: <a href="https://doi.org/10.1016/j.apcatb.2023.122885">10.1016/j.apcatb.2023.122885</a></p> <p>Folder names describe the type of data content.</p>
Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction"
<p>Raw data for the article "Unwrap Them First: Operando Potential-induced Activation Is Required when Using PVP-Capped Ag Nanocubes as Catalysts of CO₂ Electroreduction'', published in Chimia 2021 75:163, doi: <a href="http://doi.org/10.2533/chimia.2021.163">10.2533/chimia.2021.163</a></p> <p>Folder names describe the type of data content.</p>
CA-discharge data set, scripts and raw data
<p>Gauge locations of 295 gauges in Central Asia, including long-term norm discharge, basin outlines and basin characteristics compiled from 3rd-party data. Discharge time series for 135 gauge locations collected from the hydrological yearbooks of Hydrometeorological Organisations in Central Asia. </p> <p>Instructions of how to use the data can be found in the readme document in the folder CA-data-paper-scripts. </p> <p>! Important note: Please do not use the glacier thinning rates extracted from Hugonnet et al., 2021 (https://doi.org/10.1038/s41586-021-03436-z), i.e. features gl_dmdt_km3a and gl_dmdtda_mma. The ice density is not accounted for in our averages. </p>
Raw Data of the Surveys conducted in the Advanced Inorganic Lab Course in the Winter Terms 2020/2021, 2021/2022, and 2022/2023 at RWTH Aachen University
<p>In the advanced inorganic lab course at RWTH Aachen University, the undergraduate students are asked to use the electronic laboratory notebook (ELN) Chemotion and thus become aware of and familiar with research data management (RDM) at an early stage in their studies. To map the implementation of research data management and the Chemotion ELN in the lab course, a survey was conducted in the winter terms 2020/2021, 2021/2022, and 2022/2023 to ask the students to share their experiences and criticism on these topics.</p> <p>In this data publication, the underlying raw data of the surveys (as received from the survey software SoSci Survey<sup>1</sup>) are available as .csv-files separated into data, values, and variables for the respective winter terms. Additionally, the evaluated data are summarized in .xlsx-files which are also part of this data publication. As the principal language of the inorganic lab course is German, the survey and it's evaluation are primarily in German language, too. For further information, please have a look at the 01_Read-me.txt file.</p> <p>The survey and the related results and interpretations are available as a journal publication elsewhere.</p> <p><strong>Literature:</strong></p> <p>1. Leiner, D. J. <em>SoSci Survey (Version 3.2.12 and newer) [Computer software]</em>. 2020.<strong> </strong><a href="https://www.soscisurvey.de">https://www.soscisurvey.de</a><em> </em>(accessed 2023-07-26).</p> <p> </p>
Raw data of healthy young adults in the Weather Prediction Task
<p>Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339</p>
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
Analysis raw data
<p>Analysis raw data of feedstock used for biochar and COMBI production and products obtained by pyrolysis and composting processes.</p>
Windrows temperature raw data
<p>Temperature data of windrows during test 1 (Summer 2018) and test 2 (Winter 2019). Climate data for the tests location (temperature and relative humidity) are included.</p>
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Modern Fortran Survey 2019 Raw Data
<p>We've organized an online survey about usage of Modern Fortran features (2003/2008 standard) on https://modernfortran.limequery.org</p> <p>The survey was online from June 2019 to March 2020 and includes 140 complete responses and 85 incomplete.</p> <p>This package contains the raw data and the automatically generated statistics from it:</p> <ul> <li>html-forms.zip .. the survey exported as a static HTML page (the original survey hid subquestions based on previous answers)</li> <li>limesurvey-archive.lsa .. the complete survey (including the results) in LimeSurvey archive format (ZIP file with proprietary files)</li> <li>printed-forms.pdf .. the survey as PDF</li> <li>results-{all,complete-only,incomplete-only}.pdf .. the automatically generated statistics of the survey results including plots. "all": include all surveys, "complete-only": only surveys which were completed, "incomplete-only": only surveys which were not completed</li> <li>results-{complete,incomplete}_only.csv .. the results in CSV format for the respective subsets</li> </ul>
Raw SNR data for Manuscript "GPS Interferometric Reflectometry : Using a Low Cost Antenna to Measure Water Levels"
<p>Raw GPS L1 SNR (and ancillary) data for an experiment to use a low-cost GPS antenna/receiver to measure water levels using the GNSS - Interferometric Reflectometry technique.</p> <p>The data were recorded at the RNLI lifeboat station in Sligo, Ireland (N 54<sup>o </sup>18' 17.8'', W 8<sup>o</sup> 34' 5.4'' ) using a Globalsat BU353S4 USB puck that uses a SirfStar IV receiver with patch antenna (2018 data) and a Maestro A2200A SirfStar IV module (2019 data). Both systems were mounted to a radio mast at around 16m above sea level.</p> <p>The data are stored in daily files with the naming convention sligDDD0.YY.TNR.gz where DDD is the Day of Year and YY is the year in short format (18,19). Each file is gzipped. </p> <p>The files are flat text files with fixed width columns in the following order</p> <p>1) PRN GPS satellite code</p> <p>2) Elevation (degrees)</p> <p>3) Azimuth (degrees)</p> <p>4) Seconds of Day</p> <p>5) change in elevation angle with time (degrees/second) : needed for reflector height change corrections</p> <p>6) Blank</p> <p>7) S1 SNR signal (dB-Hz)</p> <p>8) Blank reserved for S2 SNR signal</p> <p>9) Blank reserved for S5 SNR signal</p>
Raw data for High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes
<p>In the following the raw data lying the foundation of the paper High Temperature Photochromism of Fe-Doped SrTiO3 Caused by UV Induced Bulk Stoichiometry Changes (Viernstein et al.) published in Advanced Functional Materials Vo. 29 Issue 23 (WILEY-VCH Verlag GmbH & Co. KGaA, Germany) in 2019 are described. They were obtained under the funding provided by Austrian Science Fund (FWF) (project F4509-N16, FOXSI) and the European Union`s Horizon 2020 research and innovation program under the grant agreement No. 824072 and consist of UV/VIS spectra, van der Pauw measurements, electrochemical impedance spectra, and laser ablation ICP-MS data.</p> <p>The UV/VIS measurements were carried out in air, at 440 °C using a deuterium and a tungsten lamp (Edmund Optics Inc., Germany) as light source and an Ocean Optics QE6500 (Halma plc, England) as spectrometer. The data include background, I<sub>0</sub> and I absorption measurements of Fe doped SrTiO<sub>3</sub> (STO) single crystals before, during, and after illumination with UV light (365 nm). The data files are labeled for example as “UVvis_FeSTO_background_1” or ”UVvis_FeSTO_I_440C_UVon_90s”, to state the type auf measurement, temperature, and status of the experiment. In each of them the average of 30 spectra is given and each exhibits two columns, namely wavelength, and intensity.</p> <p>The van der Pauw measurements were performed on two Keithley 20 multimeter and a 2410 1100 V source meter (Keithley Instruments, USA). They are labeled in the following way: “date_applied voltage_atmosphere_sample identification_temperature cycle_status of the measurement”. Each file consists out of a header giving time, cycle number, temperature (real and set) and six columns, t[s], (applied) U[V], (measured) I[A], R [Ohm], and two unnamed columns ((applied) U[V] and (measured) U [V]).</p> <p>The electrochemical impedance spectra were obtained before, during, and after UV exposure, using an Electrochemical Test Station POT/GAL 30 V/2 A or a Novocontrol Alpha‐A high‐performance frequency analyzer, respectively (both Novocontrol Technologies GmbH & Co. KG, Germany). Each spectrum is named after the following description: “date_sample name_UVonoff_real temperature_atmosphere_spectra number”. A header with date, time, cycle number, and temperature followed by four columns, namely Freq [Hz], Re (real part of the impedance spectra), Im (imaginary part), Amp (amplitude), and Pha (phase) are given.</p> <p>Laser ablation ICP-MS measurements were performed on a NWR213 laser ablation system (ESI; USA) and an iCAP Q ICP-MS (Thermo Fisher Scientific, Germany). The obtained data file is labeled as Iaser_ablation_ICP_MS_FeSTO and exhibits sample names, names of the measured masses (isotopes) and the obtained counts.</p>
Raw data for "The role of conidia in the dispersal of *Ascochyta rabiei*"
<p>Raw data associated with the pre-print, “<em>The role of conidia in the dispersal of </em>Ascochyta rabiei”, <a href="https://doi.org/10.1101/2020.05.12.091827">https://doi.org/10.1101/2020.05.12.091827</a></p> <p>There are six data files associated with this manuscript. Five are raw data, one was generated as a course of the analysis, “weather_summary.csv”. All files are in .csv format. Details for each including the number of columns and column contents and units follow.</p> <p><strong>Files and Content Descriptions</strong></p> <ul> <li><strong>BCG_weather_data.csv</strong> – 15-minute interval weather data from the Birchip Ag Group automated weather station near Curyo, Victoria, Australia for the time period of 01/10/2019 to 31/10/2019</li> <li><strong>Curyo_SPA_2019_weather.csv </strong>– 10-minute interval weather data from AgVictoria’s automated weather station at Curyo, Victoria, Australia from 22/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>Dispersal_experiment_dates.csv</strong> – Data detailing each spread event at each location including the time trap plants were put out and brought in and date assessed</li> <li><strong>Horsham_SPA_2019_weather.csv</strong> – 10-minute interval weather data from AgVictoria’s automated weather station at Horsham, Victoria, Australia from 01/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>lesion_counts.csv </strong>– Data detailing lesion counts on trap plants for each location and spread event</li> <li><strong>weather_summary.csv</strong>– Summary weather data detailing for each location and spread event</li> </ul> <p><em><strong>BCG_weather_data.csv </strong></em>The file “BCG_weather_data.csv” contains six columns:</p> <ul> <li><strong>Reading Time </strong>– the time at which the data was recorded</li> <li><strong>Rainfall</strong> – the amount of rainfall (mm)</li> <li><strong>Humidity</strong> – relative humidity (%)</li> <li><strong>Temperature</strong> – air temperature (˚C)</li> <li><strong>Wind Speed</strong> – wind speed (km/p)</li> <li><strong>Wind Direction</strong> – direction in which the wind was blowing from (cardinal directions)</li> </ul> <p><em><strong>Curyo_SPA_2019_weather.csv</strong></em> and H<em><strong>orsham_SPA_2019_weather.csv </strong></em>The files “Curyo_SPA_2019_weather.csv” and “Horsham_SPA_2019_weather.csv” both contain 22 columns:</p> <ul> <li><strong>Time</strong></li> <li><strong>Air Temperature - average (ºC)</strong></li> <li><strong>Soil Temperature - average (ºC)</strong></li> <li><strong>Relative Humidity - average (%)</strong></li> <li><strong>Wind Speed - minimum (km/h)</strong></li> <li><strong>Wind Speed - average (km/h)</strong></li> <li><strong>Wind Speed - maximum (km/h)</strong></li> <li><strong>Wind Direction - average (º)</strong></li> <li><strong>Solar Radiation - average (W/m^2)</strong></li> <li><strong>Sigma - average (deg)</strong></li> <li><strong>Rainfall - (mm)</strong></li> <li><strong>Voltage - minimum (V)</strong></li> <li><strong>Voltage - average (V)</strong></li> <li><strong>Voltage - maximum (V)</strong></li> <li><strong>Apparent Temperature - average (ºC)</strong></li> <li><strong>Dew Point - average (ºC)</strong></li> <li><strong>Delta T - average (ºC)</strong></li> </ul> <p><em><strong>Dispersal_experiment_dates.csv </strong></em>The file “Dispersal_experiment_dates.csv” contains five columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>time out </strong>– date and time on which the trap plants were deployed in the paddock for the spread event (rainfall)</li> <li><strong>time removed </strong>– date and time on which the trap plants were retrieved from the paddock after the spread event (rainfall)</li> <li><strong>assessment date</strong>– date on which trap plants were assessed for number of lesions</li> </ul> <p><em><strong>lesion_counts.csv </strong></em>The file "lesion_counts.csv” contains thirteen columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>distance </strong>– Distance from infection source (m)</li> <li><strong>station – </strong>Trap plant units (number of trap plants at each point along transect)</li> <li><strong>transect – </strong>One of ten repeating lines along which the trap plant stations were deployed in a 90˚ arc downwind of the infection source</li> <li><strong>dist_stat – </strong>A concatenation of the ‘dist’ and ‘station’ columns</li> <li><strong>plant_no – </strong>Total number of plants at a given station</li> <li><strong>pot_no </strong>– Individually assigned pot number for each individual transect (1 – 56)</li> <li><strong>counts_p1 – </strong>Lesion counts for Pot 1</li> <li><strong>counts_p2 – </strong>Lesion counts for Pot 2</li> <li><strong>counts_p3 – </strong>Lesion counts for Pot 3</li> <li><strong>counts_p4 – </strong>Lesion counts for Pot 4</li> <li><strong>counts_p5 – </strong>Lesion counts for Pot 5</li> </ul> <p><em><strong>weather_summary.csv </strong></em>The file “weather_summary.csv” contains six columns that summarise the weather conditions during each of the six spread events at three experiment plot locations.</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep</strong> – Spread event number for each location</li> <li><strong>mws</strong> – Mean wind speed for the spread event (m/s)</li> <li><strong>ws_sd </strong>– Wind speed standard deviation for the spread event</li> <li><strong>mwd </strong>– Mean wind direction for the spread event (˚)</li> <li><strong>sum_</strong>rain – Total precipitation during spread event including both natural rainfall and overhead sprinkler irrigation (mm)</li> </ul>
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
PROTECT project second RAW inertial data for pedestrian inertial localisation (ORDP initiative)
<p><strong>Contact person(s)</strong>: Enrico de Marinis</p> <p><strong>Data collector(s)</strong>: Enrico de Marinis. Fabrizio Pucci, Michele Uliana</p> <p><strong>Data curator(s)</strong>: Guido Rosi</p> <p><strong>Work package leader(s)</strong>: Fabrizio Pucci; Fabio Andreucci</p> <p><strong>Content</strong></p> <p>Inertial Measurement Unit raw data in TXT open and readable format, to be used for processing and testing the pedestrian dead reckoning algorithms by the inertial and indoor tracking scientific community.</p> <p>The raw inertial data have been collected and made publicly available in the frame of the SME Phase 2 project PROTECT (820867), co-funded by the European Commission</p> <p><strong>Experimental data</strong></p> <p>The publicly shared archive contains the following, distinct datasets:</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_143538_000002_000003_008.decod.grz; collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_150213_000024_000024_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_153840_000007_000024_005.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200729_155152_000024_000003_010.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 29, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_113457_000024_000004_006.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_141622_000007_000007_003.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_20200730_153554_000007_000007_004.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.grz: collected in Pisa (Italy) with the DUNE foot-mounted sensor unit on July 30, 2020, in the frame of the project WP4 activities (system demonstration).</li> </ul> <p><strong>Images of the experimental data</strong></p> <p>For each of the above data files, the image of the corresponding PDR (Pedestrian Dead Reckoning) processed track has been added as a geo-referenced JPG capture overlaid on the location satellite image. The image file name is the same as the corresponding data file.</p> <ul> <li>RawData_20200729_141819_000002_000003_007.decod.jpg</li> <li>RawData_20200729_143538_000002_000003_008.decod.jpg</li> <li>RawData_20200729_150213_000024_000024_004.decod.jpg</li> <li>RawData_20200729_153840_000007_000024_005.decod.jpg:</li> <li>RawData_20200729_155152_000024_000003_010.decod.jpg</li> <li>RawData_20200730_113457_000024_000004_006.decod.jpg</li> <li>RawData_20200730_141622_000007_000007_003.decod.jpg</li> <li>RawData_20200730_153554_000007_000007_004.decod.jpg</li> <li>RawData_YYYYMMDD_HHMMSS_000007_000003_011.decod.jpg</li> </ul> <p><strong>Open and Accessible Data format</strong></p> <p>The data format is the following</p> <p>gyro(x) gyro(y) gyro(z) acc(x) acc(y) acc(z) mag(x) mag(y) mag(z) temperature altitude</p> <p>x, y, z indicate the axes of the Inertial Measurement Unit</p> <p>gyro stands for the angular velocity and is in rad/s</p> <p>acc stands for the acceleration and is in m/s^2</p> <p>mag is the magnetic field and is in milligauss</p> <p>temperature is in °C</p> <p>altitude is the output of the altimeter and is expressed in meters</p> <p>All the samples, in all datasets have been recorded with a 200 Hz sampling frequency.</p>
Raw and analyzed data for manuscript: "Wood surface ablation and nanostructuring using a femtosecond laser"
<p><strong>Abstract</strong></p> <p>The processing of Norway spruce and European beech wood specimens by means of femtosecond laser pulses was investigated on conditioned natural samples as well as on samples coated with beeswax or a water-borne stain. Depending on laser pulse energies and processing times, this allowed for different modes of surface modification. At low laser intensities, an etching almost without thermal impact was detected, whereas higher laser intensities led to the generation of hierarchical micro and nanostructures. The usage of argon or atmospheric air as cover gases during the laser processing had only minor effects on the surface structures. Observed differences in the etching or functionalization of the wooden surfaces mostly originated in the chemical structure of the surface finish and the physical properties of the wood substrates, such as the density or moisture content.</p>
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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)
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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.