Find research datasets worth reusing

Survey questionnaire and results on Pay-for-Performance (P4P) schemes for energy efficiency measures

The online survey was designed and conducted in the framework of the EU H2020 SENSEI project. The aim of the survey was to identify stakeholders’ perceptions on how P4P programmes could be integrated into the existing EU regulatory and market framework. We developed a semi-quantitative, self-completion online questionnaire, using the online tool “Alchemer”. The online survey collects input from different experts from the field of academia, consultancies, policymaking, and the energy industry.  The questionnaire is used by Tzani et al (2022) to investigate how policy developments and adjustments in the EU can facilitate the design of performance-based energy efficiency programmes. The study combines a Strengths, Weaknesses, Opportunities, and Threats framework with an Analytical Hierarchy Process method and a Threats, Opportunities, Weaknesses, and Strengths matrix for the analysis of different stakeholder perceptions and the formulation of policy strategies. If you use this questionnaire in an academic publication, please cite the corresponding article: Tzani, D., Exintaveloni, D.S., Stavrakas, V., Flamos, A. Devising policy strategies for the deployment of energy efficiency Pay-for-Performance programmes in the European Union.

opencc-zeroMay 2022View details →

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Data: Measuring plant attractiveness to pollinators: methods and considerations

Global pollinator declines have fostered increased public interest in creating pollinator-friendly gardens in human-managed landscapes. Indeed, studies on urban pollinator communities suggest that flower-rich greenspaces can serve as promising sites for conservation. Ornamental flowers, which are readily available at most commercial garden centers, are ubiquitous in these landscapes. These varieties are often non-native and highly bred, and their utility to pollinators is complex. In this study, we used observational data and citizen science to develop a methods framework that will assist stakeholders in the floriculture industry to incorporate metrics of pollinator health into existing breeding and evaluation protocols. The results of this study support how plant attractiveness to pollinators is often dependent on variables such as climate and plant phenology, which should be considered when developing an assessment tool. Furthermore, we found that some cultivars were consistently attractive across all observations while for other cultivars, pollinator visitation was apparently conditional. We determine using multiple statistical tests that 10 min is a sufficient length of time for observation of most plant types to broadly estimate three measures of plant attractiveness: visitor abundance, primary visitors attracted, and cultivar rank attractiveness, without sacrificing efficiency or accuracy. Additionally, we demonstrate that properly trained non-expert observers can collect accurate observational data, and our results suggest that protocols may be designed to maximize consistency across diverse data collectors.

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Data from: The shape of aroma: Measuring and modeling citrus oil gland distribution

From preventing scurvy to being part of religious rituals, citrus are intrinsically connected to human health and perception. From tiny mandarins to head-sized pummelos, citrus capability of hybridization provides a vastly diverse array of fruit sizes and shapes, which in turn corresponds to a diversity of flavors and aromas. These sensory qualities are tightly linked to oil glands in the citrus skin. The oil glands are also key to understanding fruit development, and the essential oils contained by them are fundamental in the food and perfume industries. We study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of the skin compared to the rind or the flesh. Second, we model the fruit shape as an ellipsoidal surface, and later we study and infer possible oil gland distributions on this surface using principles of directional statistics. We finally compare and contrast these overall fruit shape models along their gland distributions across different citrus species. This morphological modeling will allow us later to link genotype with phenotype, furthering our insight on how the physical shape is genetically specified in DNA.

opencc-zeroMay 2022View details →

Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints' Kinematics (Experimental data)

Experimental data from "Problems Using Data Gloves with Strain Gauges to Measure Distal Interphalangeal Joints’ Kinematics", available in Sensors.   "PHASE I - STATIC POSTURES, FREE MOTION AND GRASPING TASKS.xlsx"  contains raw data of CyberGlove data glove of 22DoF while performing experiments detailed in Phase I. Jonts labelled as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>.  Task order detailed in "PHASE I TASK ORDER.txt". Subjects' hand length detailed in "PHASE I SUBJECT DATA.txt".   "PHASE II - SOLLERMAN HAND FUNCTION TEST.xlsx"  contains joint angles recorded using CyberGlove data glove of 22DoF while performing experiments detailed in Phase II. Jont angles and sign criteria considered as in <a href="https://www.nature.com/articles/s41597-019-0175-6">Human hand kinematic data during feeding and cooking tasks</a>. Subjects' hand length and laterality detailed in "PHASE II SUBJECT DATA.txt".   For further information please contact authors (rodaa@uji.es).

Assets for 'Phase correlation on the edge for estimating cloud motion' submitted to Atmospheric Measurement Techniques

1. CMV-26-07-2016_ARM-SGP.gif  Sample cloud motion vectors from TSI camera images over the United States Atmospheric Radiation Measurement user facility’s Southern Great Plains site. 2. raindrop_02-01-2017_ARM-SGP.gif Rotation of cloud motion vectors from raindrop contaminated TSI camera.

A dataset of measured spatial room impulse responses in different rooms including visualization

An open-source dataset of captured spatial room impulse responses (SRIRs) is presented. The data was collected in different enclosed spaces at the Technische Universität Ilmenau using an open self-build microphone array design following the spatial decomposition method (SDM) guidelines. The included rooms were selected based on their distinctive acoustical properties resulting from their general build and furnishing as required by their utility. Three different classes of spaces can be distinguished, including seminar rooms, offices, and classrooms. For each considered space different source-receiver positions were recorded, including 360° images for each condition. The dataset can be utilized for various augmented or virtual reality applications, using either a loudspeaker or headphone-based reproduction alongside the appropriate head-related transfer function sets. In future, we plan to add more rooms and more source-receiver positions. Please cite our corresponding paper: Klein, F., Surdu, T., Aretz, A., Birth, K., Edelmann, N., Seitelman, F., Ziener, C., Werner, S., and Sporer, T., “A dataset of measured spatial room impulse responses in different rooms including visualization,” in 152nd AES Convention, 2022, https://www.aes.org/e%E2%80%90lib/browse.cfm?elib=21728

opencc-by-4.0Feb 2021View details →

Measurement and line parameter database CO2 6000-7000 cm-1

New self-broadened CO2 measurements and analysis are published in “High accuracy CO2 Fourier transform measurements in the range 6000–7000 cm-1”, (Birk M., Röske C., Wagner G., High accuracy CO2 Fourier transform measurements in the range 6000–7000 cm-1, Journal of Quantitative Spectroscopy & Radiative Transfer, 272, 2021, 107791, special issue for HITRAN2020 database, https://doi.org/10.1016/j.jqsrt.2021.107896). Zenodo serves as a repository for the measurement database and the line parameter database, replacing the commonly used supplement in the paper. A new version V1.1 has been created containing the line parameter database. Readme and measurement database in the V1 version are still valid. The reason for the update is that a wrong version of the database was uploaded, containing the originally fitted line parameters without extended uncertainties. The readme refers to the new version. The V1.1 line parameter database is in “CO2_line_parameters_6000-7000cm-1_4HITRAN_V6.1.1.zip”.

EMNIST-DA: A dataset for studying measurement shift

emnist_large.tar.gz contains the EMNIST-DA dataset consisting of 13 shifted versions of the 47-class extended-MNIST (EMNIST) dataset. As many methods achieve very good performance on MNIST datasets, this dataset was created to be more challenging with 47-classes and some difficult (measurement) shifts. Source code to generate the dataset is available at https://github.com/cianeastwood/bufr/blob/main/data/emnist.py.

opencc-by-4.0Feb 2022View details →

Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity

# Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433 ## Contents * <TMD-material>: directories with raw and derived data for all 10 TMDs * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities * plot_*_v1.m: Matlab scripts for plotting data ## Description of the data The raw and derived data stored in directories <TMD> contain the following files: * <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization). The structure of the data file names is as follows: <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements. The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines): * plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2 * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows: wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx;  real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;

Ice Core Measurements - Northern Norwegian Fjord Ice - Winter 2018/2019

Dataset from the 2018-2019 field season in six northern Norwegian fjords including ice bulk salinity and d18O, seawater salinity and d18O, and river water d18O. The fjords included are Beisfjord (Nordland), Lavangen (Nordland), Nordkjosbotn (Tromsø), Storfjord (Tromsø), Storfjord (Tromsø), Ramfjord (Tromsø), and Kattfjord (Tromsø).

opencc-by-4.0Jul 2022View details →

Phonon calculations and measurements dataset for use with the Euphonic program

A dataset of ab initio phonon calculation input and output files for the CASTEP, VASP and PHONOPY codes, together with inelastic neutron scattering measurements from the MERLIN and MARI spectrometers at the ISIS Neutron and Muon Source. The dataset comprises data on five materials (Aluminium, La2Zr2O7, Niobium, Quartz and Silicon) as shown in the publication "Euphonic: inelastic neutron scattering simulations from force constants and visualisation tools for phonon properties" by R. Fair et al., and is organised into folders for each material. There are also accompanying python and matlab scripts for handling the datafiles. The main zip file, <code> euphonic_data.zip</code>, contains DFT output files (*.castep_bin, and phonopy.yaml) which can be used with the Euphonic python program ( https://euphonic.readthedocs.io/ ) which has been developed to efficiently calculate phonon bandstructures and inelastic neutron spectra. It also contains INS measured powder datafiles for Aluminium and Silicon and scripts using the Mantid program to read and plot these data. Scripts for handling the measured single-crystal INS data on Quartz and the small subset of the data shown in the publication are included in <code>euphonic_data.zip</code> but the full datafiles are in a separate archive, <code>quartz_raw_data.zip</code>.

Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space).

Data set from Correlations between a Shintergy synchronized brain and a laser eld; a possible fractal structure of Consciousness (Part I of 7 – Local measure in time and space), and figures.

Measured data of article in J. Vac. Sci. Technol. A 39, 052401 (2021); doi: 10.1116/6.0001126

The folder contains raw and fitted data of the in 2021 published article: Wafer-level uniformity of atomic-layer-deposited niobium nitride thin films for quantum devices, Authors: Emanuel Knehr, Mario Ziegler, Sven Linzen, Konstantin Ilin, Patrick Schanz, Jonathan Plentz, Marco Diegel, Heidemarie Schmidt, Evgeni Il’ichev, and Michael Siegel, Journal: Journal of Vacuum Science & Technology A 39, 052401 (2021); <a href="https://doi.org/10.1116/6.0001126">https://doi.org/10.1116/6.0001126</a> The names of the 13 data files include the corresponding figure numbers of the published article.

KPIs to measure the impact of Food loss and Waste prevention strategies

These data correspond to the process of KPIs definition to measure the impact of Food Loss and Waste prevention strategies in FOODRUS. Both the initial long list of KPIs that started the process, and the results of 3 different surveys delivered to experts and stakeholders are included here.

Effect of sediment suspensions on seawater conductivity measurements

Data are in small files recorded in April 2015 and March 2016 during experiments made in Shom's metrology laboratory. A 2 liter cylindrical container, immersed in a calibration bath, was filed with seawaters of practical salinities 35 or 38, and stabilized in temperature at 10 °C to better than 1 mK. 10 °C was choosen to avoid seawater evaporation during measurements. Measurements were made with a calibrated CTD recorder SBE 37 placed in the cylindrical container and a reference temperature probe SBE 35 placed in the calibration bath. Sand or sediments were added progressively in the container and mixed with a stirring propeller. After each increase in sand concentration, a file was recorded. Measurements were made at concentrations: 0, 50, 100, 200, 300, 500, 700, 900, 1100, 2000, 3000 and 5000 mg/l. The sand comes from Plouneour-Trez beach in Brittany, France, 48° 39′ North, 4° 19′ West. The sediments come from muds taken in the Abers Benoit (Treglonou) and Le Faou bays (France) located respectively at 48° 33′ North, 4° 32′ West and 48° 17′ 36″ North, 4° 10′ 39″ West.

A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients. The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column. Study approval and data collection Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

opencc-by-nc-nd-4.0Jun 2022View details →

BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET

BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET (MONZA) 10.5281/zenodo.6783695 Released under CC BY-NC-ND 4.0 - https://creativecommons.org/licenses/by-nc-nd/4.0/ H2020 BIM4EEB Project  <a href="https://www.bim4eeb-project.eu/">https://www.bim4eeb-project.eu/</a> https://zenodo.org/communities/bim4eeb_eu_project 10.5281/zenodo.6783695 BIM4EEB - BIM based fast toolkit for Efficient rEnovation in Buildings This project has received funding from European Union's H2020 research and innovation programme under grant agreement N. 820660. The content of this document reflects only the author's view only and the Commission is not responsible for any use that may be made of the information it contains.

Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities

This repository contains raw data and analysis routines of the publication “Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities” in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a>). The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes. For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length. For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step. The table below summarizes all datasets and Python scripts uploaded to this repository. <table align="center"> <tbody> <tr> <td> Name </td> <td> Applicability </td> <td> Description </td> </tr> <tr> <td> Dataset, 12-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg. </td> </tr> <tr> <td> Dataset, 16-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg. </td> </tr> <tr> <td> Dataset, 20-04-2021.zip </td> <td> Ideal scan alignment </td> <td> Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg. </td> </tr> <tr> <td> Dataset, 18-05-2021.zip </td> <td> Arbitrary alignment </td> <td> Dataset for alignment angle of 2.7 deg. </td> </tr> <tr> <td> Chirp.data </td> <td> Both methods </td> <td> File containing k-interpolation data </td> </tr> <tr> <td> ReadOCTFile.py </td> <td> Both methods </td> <td> Written by Jos de Wit, this module reads and imports spectra from raw OCT files. </td> </tr> <tr> <td> DataProcessing.py </td> <td> Both methods </td> <td> This module contains all analysis and processing routines. </td> </tr> <tr> <td> Diffusion.py </td> <td> Both methods </td> <td> This script determines diffusion coefficient from raw OCT spectra. </td> </tr> <tr> <td> Beam Waist.py </td> <td> Both methods </td> <td> This script determines focus beam waist from raw OCT spectra. </td> </tr> <tr> <td> Angle.py </td> <td> Ideal scan alignment </td> <td> This script determines Doppler angle from raw OCT spectra. </td> </tr> <tr> <td> Flow.py </td> <td> Ideal scan alignment </td> <td> This script determines M-scan and B-scan flow profiles from raw OCT spectra. </td> </tr> <tr> <td> Omnidirectional.py </td> <td> Arbitrary alignment </td> <td> This script determines flow profiles for arbitrary scan alignment. </td> </tr> </tbody> </table>

opencc-zeroJul 2022View details →

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Alternative Covid-19 mitigation measures in school classrooms: Analysis using an agent-based model of SARS-CoV-2 transmission

The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.