Find research datasets worth reusing

Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express

Bow shock crossings at Venus identified manually from the ASPERA-4 and MAG instruments onboard Venus Express for the full mission from 2006 to 2014. A detailed description of the dataset can be found in the paper "Influence of solar wind variations on the shapes of Venus’ plasma boundaries based on Venus Express observations" by Signoles et al. The boundary crossings by Venus Express are determined from combining the measurements of both ion, electron and magnetic field measurements. The bow shock is identified from the sharp increase in the magnetic field magnitude, and the increase in electron and ion temperature. The ion composition boundary is identified from the decrease in magnetosheath protons and electrons, and the appearance of planetary heavy ions. The dataset contains 5193 identified bow shock crossings and 2679 identified ion composition boundary crossings. For more information on the dataset contact: M. Persson, moaperssonphd at gmail.com

Inner filter effect correction for fluorescence measurements in microplates (AddAbs) - experimental data

Experimental data for the paper entitled Reducing the Inner Filter Effect in Microplates by Increasing Absorbance? Linear Fluorescence in Highly Concentrated Fluorophore Solutions in the Presence of an Added Absorber (<a href="https://doi.org/10.1021/acs.analchem.3c01295">https://doi.org/10.1021/acs.analchem.3c01295</a>). Separate worksheets are provided for the following: 1. Fluorescence measurements - raw values in triplicate; 2 worksheets for transparent (T) and nontransparent (NT) microplates, each with 15 titrations: L1-L12 and H1-H3, 2. Absorbance measurements - raw values in triplicate (λex = 345 nm, λem = 390 nm); 1 worksheet for T microplates only, with 4 titrations: L1-L4, 3. Fluorescence measurements - averaged, baseline-corrected averaged and normalized baseline-corrected averaged triplicates; 2 worksheets for T and NT microplates, each with 15 titrations: L1-L12 and H1-H3, 4. ZINFE/NINFE-corrected fluorescence in T microplates; 12 worksheets for 12 titrations: L1-L9 and H1-H3, 5. ZINFE/NINFE-corrected fluorescence in NT microplates; 12 worksheets for 12 titrations: L1-L9 and H1-H3. The results of the ZINFE and NINFE correction methods obtained using the online calculator service written in Javascript: https://ninfe.science (version 09.5.2022.) For additional details please visit: https://glymech.pharma.hr//GlyMech.html.

Dataset: Strauss et al. 2023 Sustainable soil management measures: a synthesis of stakeholder recommendations

The provided dataset contains the used information for the scientific publication Strauss, V., Paul, C., Dönmez, C., Löbmann, M., & Helming, K. (2023). Sustainable soil management measures: a synthesis of stakeholder recommendations. Agronomy for Sustainable Development, 43(1), 17   Due to the assessment language, parts of the dataset are in German. Specifically, it contains: - Stakeholder documents (welche ausgewertet wurden) & Einteilung in Stakeholder Groups - Erfassung der Aussagen & Kategorisierung - Auswertung zur Longlist - Ergebnisse der Farmer Survey

opencc-by-4.0Jan 2023View details →

Transformer Inrush Measurements in a Distribution Grid Laboratory

This data set contains measurement data of distribution transformer inrush transients obtained in the Distribution Grid Laboratory at the IAEW at RWTH Aachen University. Within the framework of the investigations, a medium voltage feeder with one or multiple medium/low voltage transformers is energised. The setup is either supplied by the public medium voltage grid or by a grid-forming converter. In the latter case, grid voltage distortions can be observed during the inrush transient due to output current limitation of the converter. The inrush measurements were carried out within the framework of the project DiSCo (Distribution System Inrush From Grid-Forming Converters). The authors gratefully acknowledge funding by E.ON SE as well as the technical support in development, execution and analysis of the experimental investigations by Westnetz GmbH. In addition, we would like to thank the colleagues of the Institute for Power Generation and Storage Systems (PGS) for their support and the informative discussions.

opencc-zeroMar 2023View details →

Where should they come from? Where should they go? Several measures of seed source locality fail to predict plant establishment in early prairie restorations

<ol> <li>During the "decade on restoration," we must understand how to reliably re-establish native plant populations. When establishing populations through seed addition, practitioners prioritize obtaining seed from locations geographically near the restoration site (i.e., "local seed sourcing"). They are assumed to be under similar environmental conditions to the restoration site and should establish more robust plant populations and preserve local biotic interactions than seeds sourced from further away. However, this assumption remains virtually untested in realistic restoration settings and the importance of seed sourcing, relative to other factors such as seeding rate and management regimes, is unclear.</li> <li>To determine if seed source impacts plant establishment, abundance, and phenology, we developed a partnership between university researchers and a native seed producer that kept records on where their seed was sourced from and where it was planted. At each site, we recorded the abundance and phenological stage of five commonly used tallgrass prairie restoration species seeded at 24 sites undergoing restoration across Michigan. We considered two measures of seed source locality: geographic distance (seeds were sourced from locations 6–750km away from their respective restoration sites) and climate distance. We also obtained data on the seeding rate and post-seeding management efforts at each site.</li> <li>We found that no measure of seed source locality predicted the likelihood of plant establishment or abundance at restoration sites. However, sites sown with seed from further away, or from cooler and wetter climates, had a greater proportion of flowering individuals earlier in the season. Finally, sites with higher seeding rates had greater plant abundance, and post-seeding management of the restoration site increased the likelihood a species would establish by 36%.</li> <li>Overall, these results suggest that seed sourcing did not impact plant establishment or abundance in our system. However, using fewer local seed sources can alter flowering phenology.</li> <li>Our results suggest that tallgrass prairie restoration efforts should prioritize higher seeding rates, post-seeding management, and might expand the region seed sources are considered "local", though this could impact flowering phenology. Future research leveraging native seed producer records can help answer critical questions about restoration seed sourcing.</li> </ol>

opencc-by-4.0Nov 2019View details →

FT6 Anonymous (7) 8-key tenoroon: measurements, photos, endoscopic video

Dataset of FT6 Anonymous (7) 8-key tenoroon containing detailed external and internal measurements, photos, and endoscopic video.

Data and script for: "Chronic and immediate refined carbohydrate consumption measured by glycemic load, and facial attractiveness"

Data and script for statistical analyses. Script: script.Rmd Data: - <a href="https://zenodo.org/api/files/5769d185-6fe1-44c1-a9b4-c36823efb4fc/data_attractiveness_juge_results.csv">data_attractiveness_juge_results.csv</a> (for GLMM)           - <a href="https://zenodo.org/api/files/5769d185-6fe1-44c1-a9b4-c36823efb4fc/data_subjects.csv">data_subjects.csv</a> (for path analysis)

Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment

Selection of the data in the following paper: Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32. This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar.  Cite this as: B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395

opencc-zeroMar 2023View details →

Measurements of nearshore waves through coherent arrays of free-drifting wave buoys

Surface gravity wave breaking occurs along coastlines in complex spatial and temporal patterns that significantly impact erosion, scalar transport, and flooding. Numerical models are used to predict these processes, but many models lack sufficient evaluation with observations during storm events. To fill the need for more nearshore wave measurements during extreme conditions, we deployed coherent arrays of small-scale, free-drifting wave buoys named microSWIFTs. The result is a large dataset covering a range of conditions. The microSWIFT is a small wave buoy with a GPS module, and Inertial Measurement Unit (IMU) used to directly measure the buoy's global position, horizontal velocities, rotation rates, accelerations, and heading. We use an Attitude and Heading Reference System (AHRS), 9 degrees-of-freedom Kalman filter to rotate the measured accelerations from the reference frame of the buoy to the Earth reference frame. We then use the corrected accelerations to compute the vertical velocity and sea surface elevation. The measurements were collected over a 27-day field experiment in October 2021 at the US Army Corps of Engineers Field Research Facility in Duck, NC. The microSWIFTs were deployed as a series of coherent arrays. They all sampled simultaneously with a common time reference, leading to a robust spatial and temporal dataset during each deployment. We evaluate wave spectral energy density estimates from individual microSWIFTs by comparing them with a nearby acoustic waves and currents (AWAC) sensor. We also compare significant wave height estimates from the coherent arrays with the nearby AWAC estimates. A zero crossing algorithm is applied to each buoy time series of sea surface elevation to extract realizations of measured surface gravity waves, yielding 116,307 wave realizations throughout the experiment. These measurements spanned offshore significant wave heights ranging from 0.5 meters to 3 meters and peak wave periods ranging from 5 to 15 seconds over the entire experiment.

Dataset in support of "Laboratory wave and stress measurements quantify the aerodynamic sheltering in extreme winds" by Tan et al. (2023, JGR: Oceans)

Data introduction:     There are three datasets used in this research: dataset 1 from Wind-Only (WO) experiment, dataset 2 from JONSWAP experiment with 10-cm significant wave height (J10), and dataset 3 from monochromatic wave experiment with 7.5-cm amplitude (M7.5).     Each dataset contains quality-controlled data of the respective experiment mentioned above. The data files are in the mat (MATLAB) format. There are 9 mat files in each dataset, and each file represents data collected under a specific wind forcing condition, with the fan frequency in the 10-50 Hz range with 5 Hz interval.     Each file contains four variables: seg (water elevation time series collected by the wave-wire with the units of m, demeaned and detrended), U (along-tank, downwind component of wind sampled by the IRGASON anemometer with the units of m/s), V (cross-tank component of wind sampled by the IRGASON anemometer with the units of m/s), and W (vertical component of wind collected by the IRGASON anemometer with the units of m/s). All four variables were collected at a sampling frequency of 20 Hz.

opencc-zeroMar 2023View details →

The practice and promise of temporal genomics for measuring evolutionary responses to global change

Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographicic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomics studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

Data set to support the DSML measure

Two datasets for the factor analysis which has been conducted to develop the Diversity of Strategies for Motivation in Learning (DSML), as it has been published in the Behavioral Sciences Journal by Caroline Hands and Maria Limniou (2013).

GPS and hydraulic head measurement utilized in North China Plain research

This dataset contains the raw data of the GPS and hydraulic head measurement  utilized in North China Plain research. ## Included files The `gps_cmonoc.dat` file contains the horizontal and vertical velocities of  the 35 continuous GPS stations from the Crustal Movement Observation Network of  China (CMONOC) project. The `gps_bjcors.dat` file contains the horizontal and vertical velocities of  the 14 continuous GPS stations from the Beijing Continuously Operating Reference  Station (BJCORS) network. The `gps_campaign.dat` file contains the horizontal and vertical velocities of  the 432 campaign GPS stations from the Crustal Movement Observation Network of  China (CMONOC) project. The `hydraulic_datacenter.xlsx` file contains the 559 measurements from confined  well accessed from the National Earth System Science Data Center, National Science  & Technology Infrastructure of China (http://www.geodata.cn), recording during 2005-2018. The `hydraulic_yearbook.xlsx` file contains the 130 measurements from both confined and unconfined well compiled from the yearbook 'the China Groundwater Level Yearbook  for Geo-environmental Monitoring', recoding during 2005-2016.

opencc-zeroApr 2023View details →

Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)

<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, "A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress). </div>

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations - Dataset

This repository contains the data used for the analysis of the paper "Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution - Part B - Particle Number Concentrations (PNC)" which is under submission.   The experimental conditions and the instruments used are detailed in Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. Sensors 2020, 20, 2219. https://doi.org/10.3390/s20082219 The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used during this study, please refer to the methods section of the paper.   sensors_raw.csv contains the following headers: <ul> <li>Bin0 to Bin15: Alphasense OPC-R1 particle number concentrations for different size bins</li> <li>Bin[1-3-5-7]MToF: mean time of flight of particles within the corresponding size bins of the Alphasense OPC-R1</li> <li>Checksum: checksum of the Alphasense OPC-R1</li> <li>SFR: sample flow rate of the Alphasense OPC-R1</li> <li>Humidity: relative humidity measured by the Alphasense OPC-R1</li> <li>Temperature: temperature measured by the Alphasense OPC-R1</li> <li>SamplingPeriod: sampling period of the Alphasense OPC-R1</li> <li>gr03um, gr05um, gr10um, gr25um, gr50um, gr100um: PNC measured by the Plantower PMS5003</li> <li>n05, n1, n25, n4, n10: PNC measured by the Sensirion SPS30</li> <li>humidity: relative humidity measured by a Sensirion SHT-3x</li> <li>temperature: temperature measured by a Sensirion SHT-3x</li> <li>sensor: id of the sensors</li> <li>site: name of the air quality monitor hosting the sensors</li> <li>exp: name of the experiment conducted</li> <li>source: source used to generate PM (incense or candle)</li> <li>variation: whether the sensors were exposed to stable or peak concentrations of PM pollution</li> <li>date: date in format yyyy-mm-dd HH:MM:SS</li> </ul> For more explanations about the fields of individual sensors, please refer to their manual (Alphasense OPC-R1: https://kolegite.com/EE_library/datasheets_and_manuals/sensors/OPC/072-0500_OPC-R1_manual_issue_1_250219.pdf ; Plantower PMS5003: https://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf ; Sensirion SPS30: https://sensirion.com/products/catalog/SPS30/)   ops.csv and ops.rds contains the readings from the OPS with the following cut sizes for the bins: <ul> <li>Bin 1 Cut Point (um),0.300</li> <li>Bin 2 Cut Point (um),0.374</li> <li>Bin 3 Cut Point (um),0.465</li> <li>Bin 4 Cut Point (um),0.579</li> <li>Bin 5 Cut Point (um),0.721</li> <li>Bin 6 Cut Point (um),0.897</li> <li>Bin 7 Cut Point (um),1.117</li> <li>Bin 8 Cut Point (um),1.391</li> <li>Bin 9 Cut Point (um),1.732</li> <li>Bin 10 Cut Point (um),2.156</li> <li>Bin 11 Cut Point (um),2.685</li> <li>Bin 12 Cut Point (um),3.343</li> <li>Bin 13 Cut Point (um),4.162</li> <li>Bin 14 Cut Point (um),5.182</li> <li>Bin 15 Cut Point (um),6.451</li> <li>Bin 16 Cut Point (um),8.031</li> <li>Bin 17 Cut Point (um),10.000</li> </ul> nanotracer.csv and nanotracer.rds contain the measurements from the Nanotracer: <ul> <li>N.1.: particles/cm3</li> <li>dp_avg.1.: mean diameter of the particles (nm)</li> <li>P.1.:</li> <li>S_al.1.: Lung Deposited Surface Area in um2/cm3</li> </ul>   experimental_conditions.csv and experimental_conditions.rds contain the end dates and start dates of each of the experiment conducted.     "pm100_cf1","pm10_cf1","pm25_cf1"

Dataset of acoustic intensity vector measurements around an upscaled ear model

A dataset of acoustic vector (particle velocity vector and scalar sound pressure) measurements of the sound field around an upscaled model of an ear. Data collected in July 2022 at the Aalto Acoustics Lab in Espoo, Finland. See the companion paper at AES for information about the contents of the dataset, measurement methodology, and example scripts. See the companion repository <a href="https://github.com/aaron-geldert/upscaled-ear-model-scripts">github.com/aaron-geldert/upscaled-ear-model-scripts</a> for example MATLAB scripts using the dataset. Correspondence should be directed to <a href="mailto:aarongeldert@gmail.com?subject=RE%20Big%20Ear%20Dataset%20(Zenodo)">Aaron Geldert (aarongeldert@gmail.com)</a>.

opencc-by-4.0Sep 2022View details →

Position Measurement of a Levitated Nanoparticle via Interference with Its Mirror Image

Interferometric methods for detecting the motion of a levitated nanoparticle provide a route to the quantum ground state, but such methods are currently limited by mode mismatch between the reference beam and the dipolar field scattered by the particle. Here we demonstrate a self-interference method to detect the particle’s motion that solves this problem. A Paul trap confines a charged dielectric nanoparticle in high vacuum, and a mirror retro-reflects the scattered light. We measure the particle’s motion with a sensitivity of 1.7×10−12  m/√Hz, corresponding to a detection efficiency of 2.1%, with a numerical aperture of 0.18. As an application of this method, we cool the particle, via feedback, to temperatures below those achieved in the same setup using a standard position measurement.

Performance measurements for "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs"

The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods. This comparison is based on performance measurements that were collected on the eX3 cluster, a Norwegian, experimental research infrastructure for exploration of exascale computing. These performance measurements are gathered in the data set provided here, particularly related to the performance of two SpMV kernels with respect to 490 sparse matrices, 6 matrix orderings and 8 multicore CPUs. Experimental results are provided in a human-readable, tabular format using plain-text ASCII. This format may be readily consumed by gnuplot to create plots or imported into commonly used spreadsheet tools for further analysis. Performance measurements are provided based on an SpMV kernel using the compressed sparse row (CSR) storage format with 7 matrix orderings. One file is provided for each of 8 multicore CPU systems considered in the paper:   1. Skylake: csr_all_xeongold16q_032_threads_ss490.txt   2. Ice Lake: csr_all_habanaq_072_threads_ss490.txt   3. Naples: csr_all_defq_064_threads_ss490.txt   4. Rome: csr_all_rome16q_016_threads_ss490.txt   5. Milan A: csr_all_fpgaq_048_threads_ss490.txt   6. Milan B: csr_all_milanq_128_threads_ss490.txt   7. TX2: csr_all_armq_064_threads_ss490.txt   8. Hi1620: csr_all_huaq_128_threads_ss490.txt A corresponding set of files and performance measurements are provided for a second SpMV kernel that is also studied in the paper. Each file consists of 490 rows and 54 columns. Each row corresponds to a different matrix from the SuiteSparse Matrix Collection (https://sparse.tamu.edu/). The first 5 columns specify some general information about the matrix, such as its group and name, as well as the number of rows, columns and nonzeros. Column 6 specifies the number of threads used for the experiment (which depends on the CPU). The remaining columns are grouped according to the 7 different matrix orderings that were studied, in the following order: original, Reverse Cuthill-McKee (RCM), Nested Dissection (ND), Approximate Minimum Degree (AMD), Graph Partitioning (GP), Hypergraph Partitioning (HP), and Gray ordering. For each ordering, the following 7 columns are given:   1. Minimum number of nonzeros processed by any thread by the SpMV kernel   2. Maximum number of nonzeros processed by any thread by the SpMV kernel   3. Mean number of nonzeros processed per thread by the SpMV kernel   4. Imbalance factor, which is the ratio of the maximum to the mean number of nonzeros processed per thread by the SpMV kernel   5. Time (in seconds) to perform a single SpMV iteration; this was measured by taking the minimum out of 100 SpMV iterations performed   6. Maximum performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the minimum time out of 100 SpMV iterations performed.   7. Mean performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the mean time of the 97 last SpMV iterations performed (i.e., the first 3 SpMV iterations are ignored). The results in Fig. 1 of the paper show speedup (or slowdown) resulting from reordering with respect to 3 reorderings and 3 selected matrices. These results can be reproduced by inspecting the performance results that were collected on the Milan B and Ice Lake systems for the three matrices Freescale/Freescale2, SNAP/com-Amazon and GenBank/kmer_V1r. Specifically, the numbers displayed in the figure are obtained by dividing the maximum performance measured for the respective orderings (i.e., RCM, ND and GP) by the maximum performance measured for the original ordering. The results presented in Figs. 2 and 3 of the paper show the speedup of SpMV as a result of reordering for the two SpMV kernels considered in the paper. In this case, gnuplot scripts are provided to reproduce the figures from the data files described above.

Comparison Auralization vs. Measurements of V2500 engine flyover at take-off conditions

Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with publication: “A framework to simulate and to auralize the sound emitted by aircraft engines.” paper Nr.  C001073, InterNoise Conference, Chiba, 2023 by A. Moreau, A. Prescher, S. Schade, M. Dang, R. Jaron, S. Guérin. Examples of audio files for the simulation and auralization of two engine flyover experiments at take-off conditions (V2527 engine powering A320 civil aircraft): <ul> <li>Flyover A – experiment on DLR LNATRA research aircraft, measurements are monaural and taken with a microphone placed wall flush with the ground (no ground reflections)</li> <li>Flyover B – experiment at Berlin Airport, measurements are taken at 1.2m above ground with an artificial head equipped with two microphones, binaural recording and ground reflection.</li> </ul> Sound amplitude levels have been normalized.

FT60 Savary jeune (14) 11-key tenoroon: measurements, photos, endoscopic video

Dataset of FT60 Savary jeune (14) 11-key tenoroon, containing detailed measurements, photos, and an endoscopic video.