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Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Annex 1 – Actions and measures relevant to research integrity matched to the UK Concordat
<p>The present dataset is an Annex to the Discussion Document entitled “<a href="https://doi.org/10.5281/zenodo.6827947">Indicators of Research Integrity: An initial exploration of the landscape, opportunities and challenges</a>”. </p> <p>It consists in a longlist of actions and measures that organisations may put in place to support research integrity, building on a set of documents that we considered to represent the perspectives of the stakeholder groups mentioned in the UK Concordat to Support Research Integrity, including: </p> <ul> <li> <p>researchers; </p> </li> <li> <p>employers of researchers (i.e. bodies that conduct or host research; employ, support or host researchers; teach research students; or allow research to be carried out under their auspices); </p> </li> <li> <p>research funders; and </p> </li> <li> <p>other organisations (e.g. professional, statutory and regulatory bodies; academies and learned societies; professional and subject-specific representative bodies; journals and publishers; and organisations offering advice, guidance and support). </p> </li> </ul> <p>The table below provides an overview of the documents covered in the dataset. It should be noted that our selection of documents is not meant to imply that other efforts are of lesser importance: it is only a starting point for discussion and seeks to represent a breadth of stakeholder views. </p> <table> <tbody> <tr> <td> <p>Document </p> </td> <td> <p>Lead </p> </td> <td> <p>Main perspective(s) </p> </td> </tr> <tr> <td> <p><a href="https://ukrio.org/wp-content/uploads/UKRIO-Self-Assessment-Tool-for-The-Concordat-to-Support-Research-Integrity-V2.pdf">UKRIO Self-Assessment Tool for The Concordat to Support Research Integrity</a> </p> </td> <td> <p>UK Research Integrity Office (UKRIO) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.1371/journal.pbio.3000737">The Hong Kong Principles for assessing researchers: Fostering research integrity</a> </p> </td> <td> <p>Moher et al. (academic article) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.vitae.ac.uk/vitae-publications/reports/research-integrity-a-landscape-study">Research integrity: a landscape study</a> </p> </td> <td> <p>UK Research and Innovation (UKRI), Vitae, UK Research Integrity Office (UKRIO), UK Reproducibility Network (UKRN) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="https://wellcome.org/reports/what-researchers-think-about-research-culture">What Researchers Think About the Culture They Work In</a> </p> </td> <td> <p>Wellcome </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017.pdf">The European Code of Conduct for Research Integrity</a> </p> </td> <td> <p>All European Academies (ALLEA) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="http://www.enrio.eu/wp-content/uploads/2019/03/INV-Handbook_ENRIO_web_final.pdf">Handbook on Research Integrity</a> </p> </td> <td> <p>European Network for Research Ethics and Integrity (ENERI) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://sops4ri.eu/wp-content/uploads/Guideline-for-Promoting-RI-in-RFOs_final.pdf">Guideline for Promoting Research Integrity in Research Funding Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Research funders </p> </td> </tr> <tr> <td> <p><a href="https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/guideline-for-promoting-research-integrity-in-research-performing-organisations_horizon_en.pdf">Guideline for Promoting Research Integrity in Research Performing Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2018.1.3">Cooperation between research institutions and journals on research integrity cases: guidance from the Committee on Publication Ethics</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers and Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2019.1.4">COPE Retraction Guidelines</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers </p> </td> </tr> </tbody> </table> <p>Find more outputs of this project in the <a href="https://zenodo.org/communities/research-integrity-indicators/">dedicated Zenodo community</a>. </p>
Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station
<p>Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper. </p> <p>The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload.</p> <p>For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu. </p> <p> </p>
The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at z~8
<p>Reduced and flux calibrated JWST/NIRSpec 1D spectra for the three sources (ID_4590 at z=8.4953, ID_6355 at z=7.6643 and ID_10612 at z=7.6592) analysed in Curti et al., 2022, "The chemical enrichment in the early Universe as probed by JWST via direct metallicity measurements at 𝑧~8" (published on MNRAS, Volume 518, Issue 1, pp.425-438)</p> <p>For more details on the data processing we refer to the Section 2.1 of the paper.</p> <p> </p> <p> </p>
Complete datasets and code for "Hungry or angry? Experimental evidence for the effects of food availability on two measures of stress in developing wild raptor nestlings"
<p><strong>Abstract</strong></p> <p>Food shortage challenges the development of nestlings; yet, to cope with this stressor, nestlings can induce stress responses to adjust metabolism or behaviour. Food shortage also enhances the antagonism between siblings, but it remains unclear whether the stress response induced by food shortage operates via the individual nutritional state or via the social environment experienced. In addition, the understanding of these processes is hindered by the fact that effects of food availability often co-vary with other environmental factors. We used a food supplementation experiment to test the effect of food availability on two complementary stress measures, feather corticosterone (CORTf) and Heterophil/Lymphocyte-ratio (H/L) in developing red kite (Milvus milvus) nestlings, a species with competitive brood hierarchy. By statistically controlling for the effect of food supplementation on the nestlings’ body condition, we disentangled the effects of food and ambient temperature on nestlings during development. Experimental food supplementation increased body condition, and both CORTf and H/L were reduced in nestlings of high body condition. Additionally, CORTf decreased with age in non-supplemented nestlings. H/L decreased with age in all nestlings and was lower in supplemented last-hatched nestlings compared to non-supplemented ones. Ambient temperature showed a negative effect on H/L. Our results indicate that food shortage increases the nestlings’ stress levels through both, a reduced food intake affecting nutritional state and the nestlings’ social environment. Thus, food availability in conjunction with ambient temperature shape between- and within nest differences in stress load, which may have carry-over effects on behaviour and performance in further life-history stages.</p>
Repeated and multivariate measures of perceived distance
<p>A dataset of repeated measures of distance perception at physical distances of 7, 8, 9, 10, and 11 meters. The data are also multivariate, with five dependent measures of distance perception. This is a 5 (physical distance) x 5 (dependent measure) within-participants design with a sample size of 46. Note data is missing for 15 trials due participant and experimenter errors.</p> <p>The csv file has 230 rows and 7 columns.</p> <p><em>Subject</em>: Unique identifier for each participant. <br> <em>Physical Distance</em>: Physical distance from the participant to the target cone, in meters.<br> <em>Blindwalk Away</em>: Participants put on the blindfold after viewing the target. Next, participants took one step to the left and turned 180 degrees to face the opposite direction. Participants were instructed to walk forward until they had walked the original distance to the target. <br> <em>Blindwalk Toward</em>: Participants put on the blindfold after viewing the target. Next, participants walked forward until they thought they had reached the target cone.<br> <em>Triangulated BW:</em> Participants put on the blindfold after viewing the target. Next, participants turned right 90 degrees and walked<br> forward 5 meters. The experimenter told participants when to stop walking. Finally, participants turned to face toward the target and walked forward two steps.<br> <em>Verbal:</em> Participants stated the distance between the target cone and themselves, in feet and inches. <br> <em>Visual Matching:</em> An experimenter stood next to the target cone and walked away from the cone in a straight line that was <br> perpendicular to the extent between the target and the participant. Participants instructed the experimenter to stop walking when they thought that the distance between the target and the experimenter was equal to the target distance.</p> <p> </p> <p> </p>
Water and chlorine in the Martian subsurface along the traverse of NASA's Curiosity rover: DAN measurement profiles along the traverse
<p>This dataset contains a water map and a table of water and chlorine content in shallow Martian subsurface derived from the DAN instrument data from a landing site up to MSL sol 3333. DAN is a neutron spectrometer onboard the NASA’s Curiosity rover (see Mitrofanov, I. G., et al., (2012). Dynamic Albedo of Neutrons (DAN) experiment onboard NASA’s Mars Science Laboratory. Space Science Reviews, 170(1–4), 559–582. <a href="https://doi.org/10.1007/s11214-012-9924-y">https://doi.org/10.1007/s11214-012-9924-y</a>).</p> <p>The DAN instrument consists of two separate units: the DAN DE is the detector and electronics block, the DAN PNG is pulsed neutron generator. The DAN DE contains two proportional counters filled with <sup>3</sup>He gas for recording thermal and epithermal neutrons up to energy of 100 eV (CTN detector) and epithermal neutrons from 0.4 eV up to 100 eV (CETN detector). DAN provides two types of measurements: active and passive. When DAN DE operates in the passive mode, its detectors record the local neutron background. In the active mode the DAN PNG unit generates short pulses of 14 MeV neutrons, and DAN DE record additional counts of post-pulse emission of moderated neutrons after their interactions with nuclei of shallow subsurface.</p> <p>The results of active and passive DAN measurements along the traverse are assigned to two independent types of pixels: Pixel with Active Data (PAD) and Pixels of Passive Data (PPD). The content of water reported as Water Equivalent Hydrogen (WEH) for both kinds of pixel. The content of absorption equivalent chlorine (AEC) reported only for PAD. Statistical errors are given in each pixel. Method of active data analysis is described in Lisov, D. I., et al., (2018). Data Processing Results for the Active Neutron Measurements by the DAN Instrument on the Curiosity Mars Rover. Astronomy Letters, 44(7), 482–489. <a href="https://doi.org/10.1134/S1063773718070034">https://doi.org/10.1134/S1063773718070034</a>. The "Method of Referencing by Active Data" (MRAD) to analyze passive data was described in Nikiforov, S. Y., et al., (2020). Assessment of water content in Martian subsurface along the traverse of the Curiosity rover based on passive measurements of the DAN instrument. Icarus, 346, 113818. <a href="https://doi.org/10.1016/j.icarus.2020.113818">https://doi.org/10.1016/j.icarus.2020.113818</a>.</p> <p>PPD pixels are presented as squares in the water map, PAD are presented as circles. Table of water and chlorine contains seven values for each pixel: (1) is the successive number of pixel, (2) is a mark of its type, either PAD or PPD, (3) is longitude and (4) is latitude coordinates of the center of the pixel, (5) is the associated member of the MSL stratigraphic column, and (6) is estimated WEH values (wt.%) in PAD or PPD and (7) is estimated AEC value in PAD (wt.%).</p>
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Data from microphone to measure the noise generated by the mobilefuge
<p>The two datasets uploaded are the measurement of noise generated when the mobilefuge is placed on table with a damping pad or without a damping pad. We found that with the use of the damping pad, the noise recorded in the microphone decreased by 13dB indicating the improved stable operation of the mobilefuge.</p>
Inventory data of woody plants surveyed and measured in North Senegal (Ferlo) in 2015-2017
<p>This dataset gathers measurements from field inventory on woody vegetation carried in the sylvo-pastoral zone of Ferlo (Senegalese Sahel) in 2015-2016-2017. The data consist of dendrometric measurements, location and species for 3215 woody individuals (trees, bushes, and shrubs) belonging to 25 species and 11 families.</p> <p><strong>Sites </strong></p> <p>The study sites are located in the Northern Sandy Pastoral Region of Senegal around the deep wells of Widou Thiengoly (15.99° N, 15.32° W) and Tessékéré (15.85° N, 15.06° W). The vegetation formation is an open savanna, with a relatively low woody cover.</p> <p>For the <strong>field work of 2015</strong>, we applied a stratified sampling according to the topography and the distance to the studied deep wells. We inventoried 139 plots of 0.25 ha each. The center of each plot was marked by a gps point. The plots were located at increasing distances from the boreholes: 2, 3.5, 5, 7.5, 10, 12.5 and 15 km (20 plots per distance). For each distance, we randomly selected at least six plots in depressions (43 plots in total), the other plots being located on slopes or on hilltops, with a total vertical drop of several meters (96 plots in total). Whenever possible, the plots in each category of topography were distributed between the two soil types. In total, 86 plots were allocated to the ferruginous soils and 53 plots to the sub-arid brown red soils. </p> <p>In<strong> 2016, </strong>additional woody plants were surveyed within 10 circular plots with a variable radius between 27 m and 52 m, so that at least 10 individuals were counted for each plot. <strong>In 2017, </strong>woody plants were surveyed within 30 square plots of 0.25 ha each. Woody individuals were all geotagged in 2016 and 2017. </p> <p><strong>Field measurements</strong></p> <p>Adults woody plants (with a circumference superior to 10 cm at ground level) were inventoried within square plots (2015, 2017) or circular (2016). Species name was identified for all individuals and recorded following the taxonomic referential of the African Plant Database (version 3.4.0). Three types of dendrometric measurements were performed on woody plants: (i) circumference, measured at 30 cm from ground level, except for shrubs for which circumference was measured at ground level; (ii) tree height, measured by an ultrasonic hypsometer Vertex IV (Haglof Inc.) (iii) two perpendicular crown diameters. GPS points were taken (GPSMAP 62, Garmin Inc.) at the center of each plot (for 2015) and, in some cases for each individual (2016-2017).</p> <p><strong>Data structure and metadata</strong></p> <p>Data are encoded in a single file, using comma-delimited format and UTF-8 encoding. Each row describes one individual woody plant with its corresponding measurements. The following table presents the variables (columns) contained in the dataset.</p> <p>Shapefile format is also available (same data as the .csv).</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> </tr> <tr> <td> <p>Tree_id</p> </td> <td> <p>-</p> </td> <td> <p>Unique identifier of the individual, with a 13 characters length. The 4 characters following the “y” indicate the year of the inventory. For instance, “tr.y2015.0034” is referring to the woody plant number 34 inventoried in 2015.</p> </td> </tr> <tr> <td> <p>Plot_id</p> </td> <td> <p>-</p> </td> <td> <p>Plot identifier</p> </td> </tr> <tr> <td> <p>Plot_area_ha</p> </td> <td> <p>ha</p> </td> <td> <p>Area of the inventoried plot</p> </td> </tr> <tr> <td> <p>Species</p> </td> <td> <p>-</p> </td> <td> <p>Genus, species, subspecies names and botanical authors</p> </td> </tr> <tr> <td> <p>Family</p> </td> <td> <p>-</p> </td> <td> <p>Family name</p> </td> </tr> <tr> <td> <p>Growth_form</p> </td> <td> <p>-</p> </td> <td> <p>Shrub, bush or tree</p> <p>Growth form expresses the extent of growth and the potential branching of the main-shoot axis. In this work, we refer to three types of growth form: shrub, bush and tree. A shrub refers to a small woody plant with a height below 2 meters and multi-stemmed. A tree designates a woody plant taller than 5 to 6 meters, generally presenting a single trunk. A bush, or a dwarf tree as in Pérez-Harguindeguy et al. (2013), is the intermediary between a shrub and a tree. Its height is usually between 2 to 6 meters and it is often multi-stemmed. Because of intra-specific traits variation, the mentioned growth form is valid for our study area.</p> </td> </tr> <tr> <td> <p>Circ30_m</p> </td> <td> <p>m</p> </td> <td> <p>Circumference measured at 30 cm from ground level. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Height_m</p> </td> <td> <p>m</p> </td> <td> <p>Tree height. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Dcrown1_m</p> </td> <td> <p>m</p> </td> <td> <p>First diameter of the crown</p> </td> </tr> <tr> <td> <p>Dcrown2_m</p> </td> <td> <p>m</p> </td> <td> <p>Second diameter of the crown (perpendicular to the first diameter)</p> </td> </tr> <tr> <td> <p>Geoloc_method</p> </td> <td> <p>-</p> </td> <td> <p>Geolocation method; indicates if it is the center of the plot which was geolocated (“geoloc.plot”, for individuals in 2015) or the woody plant (“geoloc.tree”, for 2016-2017).</p> </td> </tr> <tr> <td> <p>Lat_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>North latitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geoloc.tree”.</p> </td> </tr> <tr> <td> <p>Long_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>West longitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geloc.tree”.</p> </td> </tr> <tr> <td> <p>Topography</p> </td> <td> <p>-</p> </td> <td> <p>Local topography of the plot. Indicates if the plot is located within a depression (lowland) or on a hilltop.</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>-</p> </td> <td> <p>Date of the survey: dd-mm-yy</p> </td> </tr> </tbody> </table> <p> </p>
Suomi 100 satellite's HEARER radio spectrometer's measurements on Dec. 9, 2020
<p>Metadata (included also inside the file):</p> <ul> <li>Spacecraft: Suomi 100</li> <li>Name of the instruments: HEARER</li> <li>Instrument type: Radiospectrometer</li> <li>Measurement day: Dec. 9, 2020</li> <li>HEARER's data file ID: m1_1607509173</li> <li>Data: <ul> <li>the 1st column: The number of the data point (an integer between 280000 - 319999)</li> <li>the 2nd column: Observation (an integer): <ul> <li>Measurement frequency: 7.953 MHz</li> <li>Data rate: 1000 data points per a second</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>The file prepared by Esa Kallio (esa.kallio@aalto.fi), Aalto University, Finland, on Sep. 8, 2022</li> </ul> <p> </p> <ul> <li>Miscellanous:</li> </ul> <p> (the number of the data point, -observation) plots can be found from the publication</p> <p> Kallio et al., Radar – CubeSat Transionospheric HF Propagation Observations: Suomi 100 - Satellite and EISCAT HF Facility, <em>Radio Science.</em></p>
Young people's media use and adherence to preventive measures in the "infodemic": Is it masked by political ideology?
<p>Data to replicate the publication "Young people's media use and adherence to preventive measures in the “infodemic”: Is it masked by political ideology?". This publication examines the role of political ideology and political extremism for COVID-19 information seeking and preventive behaviour with data of the COVIDisc project. COVIDisc investigates how young people aged 15 to 34 years perceive the discussion in the Coronavirus Pandemic, which messages reach them, what media they use to inform themselves and how they experience the situation. en</p>
Measurements in October 2021 using a digital magnetic variation station at the Simeiz-Katsiveli geodynamic test site
<p>Measured by the digital magnetic variation station at the Simeiz-Katsiveli test site during the period October 07–21, 2021.</p>
End-condition for solution small angle X-ray scattering measurements by kernel density estimation
<p>The set of python scripts and some datasets for estimating the minimum X-ray exposure time for X-ray solution scattering experiments using statistical and mathematical approaches.</p> <p>We apply a statistical inequality to estimate the kernel density estimation (KDE) method’s error to determine the minimum X-ray exposure time.</p> <p>Please refer to the following article, </p> <p>End-condition for solution small angle X-ray scattering measurements by kernel density estimation<br> Science and Technology of Advanced Materials: Methods, Volume 2 Issue 1, pages 426-434 (2022)<br> DOI: 10.1080/27660400.2022.2140021<br> <a href="https://doi.org/10.1080/27660400.2022.2140021">https://doi.org/10.1080/27660400.2022.2140021</a></p>
Particle number concentration and meteorological measurements at Berlin-Tegel Airport during its closure
<p>Observation data of particle number concentrations (PNC) on the airfield of Berlin-Tegel Airport (TXL) during its closure. PNC was recorded with a Grimm EDM465 UFPC, including meteorological parameters measured with a Lufft WS600-UMB.</p> <ul> <li>observations between 20. October 2020, 14:44 LT and 3 December 2020, 03:03 LT</li> <li>time interval: 5 seconds</li> <li>air inlet of CPC at 1.4 m above ground</li> <li>weather sensor at 1.3 m above the ground.</li> <li>Location: 52,561 North, 13,320 East</li> <li>Variables: <ul> <li>particle number concentration in particles/cm³ (pnc)</li> <li>wind speed in m/s (ws)</li> <li>wind direction in ° (wdir)</li> <li>air temperature in °C (temp)</li> <li>relative humidity in % (rh)</li> <li>air pressure in hPa (pressure)</li> <li>precipitation in mm (pcpn)</li> <li>date as local time</li> <li>phase: whether the airport was still open ("TXL_open") or already closed ("TXL_closed")</li> <li>no data available between between 15.11.2020 02:15:00 and 18.11.2020 11:14:55 due to hardware issues</li> </ul> </li> </ul>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load('file.npy', allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> </li> </ul> </li> <li>processed_data: <ul> <li>'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>'Data_final.npy': file containing all computed quantities</li> <li>'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'.</li> </ul> </li> </ul> <p> Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>'lat': latitude, in degree</li> <li>'lon': longitude, in degree</li> <li>'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors</li> <li>'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity</li> <li>'direction': measured wind direction, in [degrees]</li> <li>'velocity': measured wind velocity, in [m/s]</li> <li>'orientaion': dune pattern orientation, [deg]</li> <li>'wavelength': dune pattern wavelength, [km]</li> <li>'z0_insitu': chosen hydrodynamic roughness for the considered station.</li> <li>'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction</li> <li>'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set</li> <li>'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset</li> <li>'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset</li> <li>'Temperature', 't': Temperature from the pressure levels ERA5 dataset</li> <li>'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset</li> <li>'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset</li> <li>'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>'Density': Density calculated from the pressure levels ERA5 dataset</li> <li>'height': Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>'theta_ground': Averaged virtual potential temperature within the ABL.</li> <li>'delta_theta': Virtual potential temperature at the ABL.</li> <li>'gradient_free_atm': Virtual potential temperature gradient in the FA.</li> <li>'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>'kH': time series of the number 'kH'</li> <li>'kLB': time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> </p>
Propagation Measurements and Analyses at 28GHz on NSF POWDER
<p><strong>IEEE ICC 2023: </strong>Propagation Measurements and Analyses at 28GHz via an Autonomous Beam-Steering Platform</p> <p> </p><blockquote> <p>This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28-GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28-GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential characteristics of the spatial autocorrelation coefficient under distance and alignment effects.</p> </blockquote> <p></p> <p><strong>Note</strong>: <em>This is a smaller version of our dataset. The original dataset collected on the NSF POWDER testbed is approximately 400 GB. Due to Zenodo's size restrictions, the data uploaded here contains only a few of our calibration (USRP 76 dB gain) and measurement logs (fully-autonomous V2X routes onsite). To gain access to our complete dataset, please contact the authors at <bkeshav1@asu.edu> or <zhan1472@purdue.edu>. Additional measurements in our full dataset include USRP 0 dB calibration results; fully-autonomous urban-stadium-van, urban-campus-cart, and urban-presidents-circle-full-van routes; and semi-autonomous (and manual) urban-garage-cart and urban-campus-cart routes.</em></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.