Find research datasets worth reusing

opencc-by-4.0Jun 2023View details →

Measuring frontier orbital energy levels of OLED materials using cyclic voltammetry in solution - accompanying data

This dataset contains the data and the Matlab scripts to create the main figures in the published manuscript.

DEDICAT6G-5G testbed measurements-CGB

- MEASUREMENTS: Downlink Throughput (+ packet loss), RTT, Jitter, Latitude and Longitude - NR5G_SA: RSRP, RSRQ, RSSI, SINR, Band, Bandwidth

Zonal and meridional wind tides measured by Kunming meteor radar

Meteor wind radar is operating at Kunming (25.6°N, 103.8°E) since January 2008. This data set represents monthly mean zonal and meridional diurnal tides from 2008 to 2022.

opencc-by-sa-4.0Mar 2024View details →

A Dataset for Inertial Measurements of Scoliotic Patients during Timed-Up and Go Tests in Unbraced and Braced Scenarios

Repository composition: *** Dataset *** Anonymous data pertaining to each participant is stored in a single folder, named S##.  Each of these folders contains the following: 1) Raw IMU data from the G-Walk sensor (3-axis acceleration, 3-axis gyroscope, 3-axis magnetometer) recorded during the Timed-Up and Go tests in .txt files. Specifically, the 3 experimental conditions are "Unbraced", "Conventional" and "3DPrinted". Each condition was recorded three times (01, 02, 03). 2) A .xlsx file named "TUG_Metrics" with the values of the TUG metrics for each condition. 3) A .xlsx file named "Segmentation_Times" with the start and end timepoints of the TUG phases for each condition. *** Boxplot *** This is a folder containing boxplots in .png files for each TUG metric, comparing the three experimental conditions. *** Histogram *** This is a folder containing histograms in .png files for each TUG metric, comparing the three experimental conditions. *** QQ Plot *** This is a folder containing qq-plots in .png files for each TUG metric, comparing the three experimental conditions.

Replication Data for the Paper "Is there a secular decline in disruptive patents? Correcting for measurement bias"

Working Paper Title: The Illusive Slump of Disruptive Patents The repository contains replication data and scripts for the paper:  Jeffrey T. Macher, Christian Rutzer, Rolf Weder, Is there a secular decline in disruptive patents? Correcting for measurement bias, Research Policy, Volume 53, Issue 5, 2024, 104992, ISSN 0048-7333, https://doi.org/10.1016/j.respol.2024.104992 The core component of the repository is the file `replication_file.R` which is an R script to replicate all figures and tables of the paper. The script relies on multiple data files. The datasets contain CD-values for granted USPTO utility patents for the years 1976-2016. All data is available in two formats, `.fst' (from the R package fst) and `.csv'.   Dataset Descriptions Main Datasets: 1. `dat_cd5_no_trunc`: This dataset contains the CD5 index of patents based on a method without truncation.  2. `dat_cd5_trunc_1975`: This dataset contains the CD5 index of patents based on a truncation method as in Park et al. (Nature, 2023). 3. `dat_cd5_no_trunc_app_adj`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021.    Additional Datasets: 4. `dat_cd5_trunc_1985`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1985. 5. `dat_cd5_trunc_1995`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1995. 6. `dat_cd5_no_trunc_app`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021, as well as those not yet granted.  7. `age_bwc_untrunc`: This dataset contains the age of backward citations using untruncated data. 8. age_bwc_trunc: This dataset contains the age of backward citations using truncated data as in Park et al. (Nature, 2023). 9. `age_bwc_untrunc_app_adj`: This dataset contains the age of backward citations using untruncated data and considering citations of patent applications granted until 2021. 10. `dat_cd10_trunc_1975`: This dataset contains the CD10 index of patents based on a truncation method as in Park et al. (Nature, 2023).  11. `dat_cd10_no_trunc_app_adj`: This dataset contains the CD10 index of patents based on a method without truncation and including citations to patent applications granted by 2021.  12. `dat_cd2021_trunc_1975`: This dataset contains the CD index as of 2021 of patents based on a truncation method as in Park et al. (Nature, 2023).  13. `dat_cd2021_no_trunc_app_adj`: This dataset contains the CD index as of 2021 of patents based on a method without truncation and including citations to patent applications granted by 2021.    To successfully run the `replication_file.R' script, make sure all data files are in the directory and the `mainDir1' variable at the beginning of the script is set to the correct path to where the data is stored. In addition, set the `mainDir2' variable to the folder where you want to store the figures created by the script.  If you have any questions, please contact christian.rutzer@unibas.ch

Weekly Ground-based Measurements of NH3, HNO3, and Submicron Aerosol Composition from Greeley, Colorado

<div>This data was collected at the Weld County Tower in Greeley, Colorado, USA and used as part of the work submitted to JGR:Atmospheres titled "Inorganic Nitrogen Gas-Aerosol Partitioning in and around Animal Feeding Operations in Northeastern Colorado in Late Summer 2021."</div>

opencc-zeroMar 2024View details →

dryad36/100

The comparative effects of landscape-level forest fragmentation, forest area and local habitat measures on Connecticut bird communities

I studied how breeding and wintering forest bird communities across Connecticut responded to variation in habitat characteristics and particularly such landscape attributes as forest fragmentation. I surveyed birds at 1,815 points along 121 transects that traversed ca. 400 km of forest. I also made 12705 habitat measurements at survey points and computed areas of forest, non-forest, core forest and perimeter/area ratios of forest for 31,550 ha of study area. I computed sampled species richness and community density as well as individual species' population densities for each transect. Moreover, I classified species encountered as to their nest site selection, macrohabitat use, microhabitat use, migratory strategy and trophic affiliation. Based on observations of 36,702 summering individuals of 123 species and 13,742 wintering individuals of 63 species, declines in community density occurred with increasing fragmentation although species richness was often more closely associated with habitat measures. Among landscape measures, forest fragmentation had the closest association with summer community measures 67% of the time, strongly suggesting that fragmentation effects were the predominant driver of such community patterns. However, short-distance migrant density and richness, foraging generalist density and richness, edge/successional species density and richness, habitat generalist density, and Brown-headed Cowbird density showed little relationship to landscape measures. The effects of fragmentation appeared to predominate over those of simply forest extent in predicting summer and winter bird community characteristics even in the comparatively extensive forests of southern New England. Despite the importance of fragmentation effects, community and individual species measures often tended to be more closely associated with habitat measures than with those of fragmentation. In addition, few summer or winter community measures or species patterns showed any significant relationship to natural forest breaks. Winter community and species density patterns showed little relationship to any landscape measures, with particularly elevation appearing to be a principal driver of winter patterns.

Wear topography measurements: selected polymers after friction tests with steel – lubrication with ionic liquids containing CNTs

Measurement files of the topography of worn surfaces of polymers cooperating with steel and lubricated with hybrids of ionic liquids and carbon nanotubes. The files correspond to the tests presented in: Tribological tests - block-on-ring - polymers vs. AISI 4130 - IL+CNTs, IL+Cu_CNTs https://doi.org/10.5281/zenodo.10817199.

Tissue factor content measured by ELISA in different size fractions of EVs.

Figure 1. Tissue factor (TF) content measured by ELISA in different size fractions of EVs. Data expressed as pg of TF in 1 x 106 EV ± SD from eight independent experiments. Data analyzed by one-way ANOVA, * symbolizes statistical significance p<0.05. <1000nm, <450nm, <200nm indicate samples filtered using syringe-top filters and their respective pore sizes.

openmit-licenseMar 2024View details →

Uncertainty Analysis of MSL's CCT-K7.2021 Key Comparison Measurements Using Uncertain Numbers

This dataset is associated with a publication of the same name (currently submitted to Metrologia). It contains Python modules and text files and can be used to repeat the analysis described in the article. The GUM Tree Calculator (GTC) Python software package is required (version 1.4.0, or above: https://github.com/MSLNZ/GTC).

dryad36/100

Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus

For sustainable biomass production of Miscanthus × giganteus (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha-1) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha-1. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.

opencc-zeroApr 2024View details →

Presentation in ADANO 2022: Vibration measurements: Laser Doppler vibrometry (LDV)

These data include Jae Hoon Sim's presentation in Conventus Herbsttagung in der Leopoldina mit wissenschaftlicher Unterstützung der ADANO, and related documents

XPS measurements on long-term stability of organic radical thin films

XPS raw data underlying Figures 1, 2, and 3 from the publication "Long-Term Degradation Mechanisms in Application-Implemented Radical Thin Films" (DOI: 10.1021/acsami.3c02057).

Avantes AvaSpec-2048 dark signal measurements for its modeling (SN: 1311018U2, 1311019U2, 1411011U2 and 1411012U2)

Each file contains integration time, temperature and dark signal spectra registered. This data have been already resampled with a temperature step of 0.1ºC and the temperature delay have been already corrected.

DLS measurements of AuNPs and porphyrin liposomes

This dataset contains DLS (dynamic light scattering) investigations on AuNPs and porphyrin liposomes.

Features computed from physical exercises measurements

The data represents time series features from an accelerometer and gyroscope extracted from <a href="../records/10984138">Physical Exercise Measurements with Accelerometer and Gyroscope (zenodo.org)</a>. The data consists of 5 feature sets. Description of feature sets: 1.     RQA features set • "RR" - Recurrence rate • "DET" - Determinism, count recurrence points in diagonal lines of length >= lmin • "RATIO" - DET/RR • "AVG" - average length of diagonal lines of length >= lmin • "MAX" - maximal length of diagonal lines of length >= lmin • "DIV" - Divergence, 1/MAX • "LAM" - Laminarity, VLRP/TR • "TT" - Trapping time, average length of vertical lines of length >= lmin • "MAX_V" - maximal length of vertical lines of length >= lmin • "TR" - Total number of recurrence points • "DLRP" - Recurrence points on the diagonal lines of length of length >= lmin • "DLC" - Count of diagonal lines of length of length >= lmin • "VLRP" - Recurrence points on the vertical lines of length of length >= lmin • "VLC" - Count of vertical lines of length of length >= lmin Was calculated by Chaos01 R package. <a href="https://cran.r-project.org/package=Chaos01">https://CRAN.R-project.org/package=Chaos01</a> The parameters were chosen so that the embedding will create a vector of one value of each axis of the accelerometer/gyroscope measurements. Therefore the used parameters were: <table> <tbody> <tr> <td>Function argument</td> <td>Value</td> </tr> <tr> <td>embedding dimension (dim)</td> <td>3</td> </tr> <tr> <td>embedding lag (lag)</td> <td>time series length</td> </tr> <tr> <td>Minimal length of recurrence line (lmin)</td> <td>20</td> </tr> </tbody> </table> For Chaos01 we change eps argument and calculated it by following formula: <code># Calculate eps for acc and gyro Chaos 01----</code> <code>get_eps <- function(input_data, scale = 1) {</code> <code>  # Calculate eps for acc and gyro</code> <code>  eps_a <-</code> <code>    purrr::map_dbl(input_data$data,</code> <code>                   ~ .x |></code> <code>                     select(Ax, Ay, Az) |></code>                                              <code> as.matrix() |></code>                                              <code> as.vector() |></code>                                               <code> sd()) |></code>                                               <code> mean() * scale</code> <code>  eps_g <-</code> <code>    purrr::map_dbl(input_data$data,</code> <code>                   ~ .x |></code>                                             <code> select(Gx, Gy, Gz) |> </code>                                              <code> as.matrix() </code>                                             <code> as.vector() |></code>                                              <code> sd()) |></code>                                              <code> mean() * scale</code> <code>return(list(a = eps_a, g = eps_g))</code> <code>}</code> "TREND" - Trend of the number of recurrent points depending on the distance to the main diagonal. Was calculated by nonlinearTseries R package.  <a href="https://cran.r-project.org/package=nonlinearTseries">https://CRAN.R-project.org/package=nonlinearTseries</a>   All following feature sets was calculated by Python package https://tsfresh.readthedocs.io/en/latest/index.html   The used dictionary is included in file named tsfresh_autocorr_spectral_features.py.   2.     Autocorrelation features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation   3.     Spectral features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.approximate_entropy #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_coefficient #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient   4.     Mix RQA/Spectral/Autocorrelation features set   5.     Tsfresh all features set #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html   Versions of the software: Python (version 3.8.10) tsfresh = 0.20.2 R (version 4.3.2) Chaos01 = Version 1.2.1 nonlinearTseries = 0.3.0

opencc-by-4.0Nov 2024View details →

CSRM Level 2 dataset: Seismometer orientation measurements of broadband seismic stations in the China Digital Seismograph Network

This dataset contains detailed information on the azimuths of more than one thousand stations in the China Digital Seismic Network (CDSN) since 2014. Deng et al. (2024) utilized 5,456,816 three-component waveform data recorded by 1,056 broadband seismic stations of the CDSN from 2014 to 2022 to evaluate and correct the azimuths of the network. The primary research method employed was far-field P-wave polarization analysis, including principal component analysis and minimum tangential energy methods. By integrating the advantages of these two methods, they conducted a detailed analysis of the azimuths across the CDSN and carried out in-depth examinations and cause analyses for stations with significant azimuth deviations (>5°). Through a comprehensive analysis of the calculation results, network operation logs, and on-site inspections, they obtained detailed information on the azimuths of more than one thousand stations in the CDSN since 2014. Appendix I lists azimuth deviations for 956 stations, while Appendix II documents temporal variations in the azimuths for 104 stations. 本数据库包含自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息。Deng等 (2024) 依托中国数字地震台网 2014 至 2022 年间 1056 个宽频带地震台站所记录的 5,456,816 个三分量波形数据, 开展了台网方位角的评估与校正工作。研究方法主要采用远场 P 波偏振分析, 包括主成分分析和最小切向能量法。结合这两种方法的优点, 他们对中国数字地震台网的方位角进行了详细分析, 并对方位角偏差较大的台站 (>5°) 进行了深入检查与原因分析。通过对计算结果、台网运维日志及现场检查的综合分析, 他们获得了自 2014 年以来中国数字地震台网超过千个台站方位角的详细信息 (附件一列出了 956 个台站的方位角偏差, 附件二记录了 104 个随时间变化的方位角信息) 。 Reference: Deng, W., Han, G., Li, J., & Sun, L. Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network. <a href="https://doi.org/10.1785/0120240075">Paper link</a> If you face any problem or issue in the usage of this dataset, please feel free to communicate with the corresponding author Juan Li (juanli@mail.iggcas.ac.cn).

opencc-by-4.0Nov 2024View details →

Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors"

Data for "Measuring mean radiant temperature for indoor comfort assessment using low-resolution optical sensors".

opencc-by-4.0Nov 2024View details →

Real-Time Dataset of Metheorological Measurements Collected During the Project

This dataset contains a collection of real-time meteorological data during the Resisto project. This information has been recorded and is structured into different fields, each representing a specific meteorological variable of the deployed sensors in the Doñana National Park.