Find research datasets worth reusing

Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)

This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen. In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data. Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from one CPS component (i.e., production station) at one point in time. The data set contains the following files <ul> <li>low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators <ul> <li>*.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory</li> </ul> </li> <li>camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution</li> <li>camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution</li> </ul> Check the following publications to learn more about our research using the model factory: Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18 (pp. 95-107). Springer International Publishing. Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 20-26). IEEE. Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. Journal of Manufacturing Systems, 63, 575-592.

opencc-by-sa-4.0Dec 2022View details →

WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets.

opencc-by-4.0Dec 2022View details →

WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets.

Data sets: Resolving local microstructure variations in Zr tubes using EBSD and imaging

SEM images and EBSD CTF files. CTF files contain measured Euler Angles that can be used for replicating all figures presented in the paper.

A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution

A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.

opencc-by-4.0Mar 2022View details →

Efficacy of Low Temperature Nitrogen Plasma in Destroying Fungi and Aflatoxin in Maize - data set

Data that was collected during optimization of the decontamination processes in maize using Response Surface Methodology

Belief, Affect, and Cognitive Dissonance in a Simulated Election - Main Study Data Set

Data gathered using Amazon's Mechanical Turk (MTurk) task platform and the Qualtrics survey platform for a simulated election experiment focusing on belief change and affect in response to repeated counterattitudinal information exposure.

Code for the publication "DOSE - Global data set of reported sub-national economic output"

The zipped file of this repository contains code and auxiliary data to reproduce the results of the publication: Wenz et al, "DOSE - Global data set of reported sub-national economic output" The respective DOSE database, version2 can be found here: <a href="http://doi.org/10.5281/zenodo.4681305">https://zenodo.org/record/7573249#.Y_RYTnbMI2w</a> Please see the README document for descriptions of the required dependencies.

opencc-by-4.0Aug 2022View details →

RSW gun fault prediction benchmark data set (demo)

The resistance spot welding (RSW) welding gun fault prediction benchmark data set has 72 multivariate time series in the training set and 8 in the testing set. Each time series length 604800 sampled at 1 Hz with missing values and has 20 dimensions (c1-c19 and the error code). We retain the missing value and the outliers of the welding gun time series for the potential of imputation research in the future. This data set supports an academic paper named 'benchmark study for welding gun fault prediction'. Feature name and explanation: c1 :  Electrode cap offset; c2 :  Electrode force; c3 :  Electrode position; c4 :   Force build-up; c5 :  Balance pressure; c6 :  Friction; c7 :  Maximum aperture; c8 :  Maximum electrode force; c9 :  Mtart friction; c10 :  US2; c11 :  Welding point count; c12 :  Position count; c13:  Setpoints of counterbalance pressure; c14:  Setpoints of electrode force; c15 :  Setpoints of electrode position; c16:  Setpoints of sheet thickness; c17 :  Setpoints of velocity; c18:  Setpoints of force build-up; c19 :  Offset value in robot. Machine Learning Task: This dataset is suitable for a time series forecasting task, where machine learning models can be trained to predict future welding parameters based on the provided welding parameters time series in history.  Code for quick start: <a href="https://zenodo.org/record/7655025">https://zenodo.org/record/7655025</a> If you want to have an overview of the data before downloading all of it, you can download only the files with the word "Damo" in the file name. For any question, please contact 1910633@stu.neu.edu.cn

Raw data set used for a paper by Gonçalves Jr. et al.

Data set of aerosol particle compositions measured using synchrotron-based multi-element microscopic speciation of individual microparticles (Scanning Transmission X-ray Microscopy with Near- Edge X-ray Absorption Fine Structure Spectroscopy combined with Computer-Controlled Scanning Electron Microscopy) used in results at a paper by Gonçalves Jr. et al. The samples were collected during the 2014 Summer Criosfera-1 (West Antarctica) campaign.

Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis

The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis". Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.

PROLINE Exploration Data Set

This repository contains the data underlying the results concerning the exploration of reactions of the proline-catalyzed Michael addition of propanal and nitropropene presented in Bensberg, M.; Reiher, M. 2023, arXiv:2212.14135 [physics.chem-ph].

Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies",

Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies".

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.

opencc-by-4.0Jan 2023View details →

Architecture-based Uncertainty Impact Analysis to ensure Confidentiality - Data Set

Data set of the Paper "Architecture-based Uncertainty Impact Analysis to ensure Confidentiality". For more information, please see the README.md. For more information please visit https://abunai.dev

Delaunay data set learn2learn l2l for meta-learning and few-shot learning

Delaunay data set learn2learn l2l for meta-learning and few-shot learning. We split it into 3 meta-train, meta-val and meta-test sets.    For details of original data see: https://github.com/camillegontier/DELAUNAY_dataset

Data set for article "Selective deuteration as a tool for resolving autoxidation mechanisms in a-pinene ozonolysis"

The data set for publication  "Selective deuteration as a tool for resolving autoxidation mechanisms in a-pinene ozonolysis" (preprint https://doi.org/10.5194/egusphere-2022-1131). Contains raw data files for the CI-orbitrap mass spectrometer (.raw files), preprocessed files used in data analysis and creating the figures in the publication, and measurement diaries for the experiments.

Instagram data set BalanceTonPorc

The #MeToo campaign had different moments and expressions online and offline in France. The original #BalanceTonPorc continued to spread on various digital platforms. On Instagram, we identified 7 hashtags derived from #BalanceTonPorc that had at least 250 posts: #BalanceTonBahut, #BalanceTonBar, #BalanceTonHosto, #BalanceTonQuoi, #BalanceTonRappeur, #BalanceTonTiktokeur and #BalanceTonYoutubeur. We downloaded 5,797 posts to synthesize a network of 14,478 hashtags, linked by 267,507 edges that indicate the number of times each pair of hashtags is in the same post.  The resulting dataset is a list of adjacencies with hashtags that have co-occurred in the conversation, together with the variable "weight", which indicates the number of times each combination of hashtags has co-occurred. It should be noted that being an undirected network, each combination is given in the table in a unique way, regardless of the position of the values.

Data set for Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?

This data set is the basis for the publication "Wireless SAWR sensors: FFT, EMD or wavelets for the frequency estimation in one shot?", submitted to Journal of Sensors and Sensor Systems. It contains the following data: - "Scipioni_JSSS23_Fig5_WaveletChoice.txt" contains results to obtain the best wavelet for this study. For this, a SAWR (Fig. 3a) signal is noised by an additive Gaussian white noise with different SNR values. The signal is then denoised by wavelets for each SNR value. Results are the new SNR values after denoising. - "Scipioni_JSSS23_Fig9_to_15_F_Ref.txt" contains all the frequencies around F=10700 MHz chosen to test the three methods: Fourier, wavelets, EMD. - 10 files "Scipioni_JSSS23_Fig10_to_14_SAW_EMDvsWavelet_FRef_XX_Occ_100.txt" contain results of the frequency and uncertainty measurement for each noisy SAWR signal versus frequencies and SNR values. - 3 files "Scipioni_JSSS23_Fig17_Tab2_3_Experimental SAWR signal_NoX" contain the values of 3 different experimental SAWR signals.