cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vfz5pz4n8k.3
title: Datasets of captured images of 3 different devices for photogrammetry calculation comparison and integration to a laser scan point cloud

description: The present data article aims to provide the whole dataset obtained during an experiment of updating laser scan point clouds with photogrammetry meshes. It was investigated whether photos from smartphones are also appropriate for updating point clouds by using photogrammetry in a factory environment. The photos (369) taken in 08:30 min with the smartphone were taken with the App 645 PRO Mk III with the settings ISO 250, F-Stop 1.8, Shutterspeed 1/125 sec. The result was that photos from current smartphones (here tested: iPhone XS) are suitable for photogrammetry applications. Main component are the comparison of the datasets of three devices: Nikon D810 with Sigma art 24mm, iPhone 6 and iPhone XS. In this repository one dataset (photos in .TIFF) of the iPhone XS is provided. The test environment was a technical room in a factory. The data sets are used for a photogrammetry comparison. Furthermore, all settings in the RealityCapture BETA 1.0 and Meshroom 2019 2.0 software are provided. A comparison of calculation time and quality of the 3D meshes was performed. The data set of the iPhone XS can be used to compare further photogrammetry software or single algorithms. Besides the images, the point clouds of the laser scanner are provided. This also includes the combination of laser scan point cloud and photogrammetry mesh. This data in brief article is published as an co-submission of the paper "Approach for an Update Method for Digital Factory Models" in the Procedia CIRP CMS 2020. Conclusions and further information can be found in this paper.
DOI: 10.17632/wzchzbm58p.4
title: DropenVideo_V01

description: DropenVideo
Date: April 22, 2024
Author: Raziyeh Akbari, raziyeh.akbari@unimib.it
Alternative contact: Carlo Antonini, carlo.antonini@unimib.it 

DropenVideo, developed in MATLAB, as an extension of Dropen, is a fully automatic frame-by-frame video analysis code for the advancing and receding contact angle determination in quasi-static wetting experiments. 
DropenVideo provides comprehensive data, including contact position, diameter and angles, and related curves. 
This software is developed in SEFILAB at the Department of Materials Science, University of Milano-Bicocca (UNIMIB), and is available at the UNIMIB repository (10.17632/wzchzbm58p). This repository will be updated with the new modified versions of the software. 
If you use this software in your research, please cite the following journal article:

 R. Akbari, C. Antonini, Contact angle measurements: From existing methods to an open-source tool, Advances in Colloid and Interface Science 294 (2021) 102470.
DOI: 10.17632/f47vcpbnz2.1
title: Data from an experiment investigating size-space associations in both directions with verbal stimuli and vocal responses

description: This data set contains the raw data files from one experiment reported in: Richter, M. & Wühr, P. (2024). Verbal stimuli allow for symmetrical S-R priming effects between size and space. Scientific Reports. https://doi.org/10.1038/s41598-024-77806-8. Here is the abstract of the paper: The spatial-size association of response codes (SSARC) effect refers to the observation that left responses are faster and more accurate to small stimuli whereas right responses are faster and more accurate to large stimuli, as compared to the reverse assignment. The underlying spatial-size associations are strongly asymmetrical with physical size/location stimuli and vocal location/size responses and allow for regular but not reciprocal SSARC effects. Recent evidence, however, points towards an important role of stimulus mode in the emergence of reciprocal compatibility effects. We investigated the reciprocity of the SSARC effect with a different stimulus mode, namely with verbal size/location stimuli and vocal responses. In a size-location task, participants vocally responded to the words “small” or “large” by saying “left” or “right” according to a compatible (“small”-“left”/“large”-“right”) or an incompatible mapping (“small”-“right”/“large”-“left”). In a location-size task, participants vocally responded to the words “left” or “right” by saying “small” or “large” according to a compatible (“left”-“small”/“right”-“large”) or an incompatible (“left”-“large”/“right”-“small”) mapping. We observed a regular and a reciprocal SSARC effect of similar size indicating symmetrical spatial-size associations. While regular SSARC effects thus emerge with verbal and physical size stimuli, reciprocal SSARC effects only emerge with verbal but not with physical location stimuli and vocal responses. Theoretical accounts of the SSARC effect differ in whether they predict reciprocal effects and whether they can account for the effect of stimulus mode on the reciprocal SSARC effect.
DOI: 10.17632/4vnndy59fp.2
title: CODEX, a neural network approach to explore signaling dynamics landscapes

description: Supplementary data and R/Python code required to reproduce the figures from the accompanying publication: Jacques et al. "CODEX, a neural network approach to explore signaling dynamics landscapes". The pre-print available here: https://doi.org/10.1101/2020.08.05.237842 Included datasets: * Single-cell ERK/Akt activity dynamics in responses to GF treatment. Acquired in-house by Paolo Gagliardi. * p53 responses to ionizing radiation. Data kindly provided by Galit Lahav and Jacob Ornstein-Stewart. * SMAD2 response to TGFb. Data kindly provided by Alexander Loewer. * Drosophila speed movement. Data obtained from https://github.com/benfulcher/hctsa_phenotypingFly * Synthetic data
DOI: 10.17632/zpyb29zvrc.1
title: Data for: Performance Analysis of Waste-to-Energy Technologies for Sustainable Energy Generation in Integrated Supply Chains

description: The Excel-file contains the cost coeffients and other data not given in the manuscript.
DOI: 10.17632/2nfd282t7d.1
title: Green synthesis, silver nanoparticles, capping agents, antimicrobial activity

description: These data and illustrate the ability of bioactive phytochemicals to bind to nanoparticles surfaces during synthesis, thereby augmenting the antibacterial capabilities of the AgNPs
DOI: 10.17632/xwycx35grk.1
title: Predictive Maintenance of Power Substation Equipment by Infrared Thermography

description: Predictive Maintenance (PdM) is a proactive approach to equipment maintenance, which involves the use of data and technology to predict and prevent potential equipment failures before they occur. In the case of power substations, PdM is crucial for ensuring the reliability and efficiency of electrical power systems.

One of the most effective techniques used in PdM is Infrared Thermography (IRT). IRT is a non-contact technology that uses thermal imaging to detect and analyze temperature variations on the surface of equipment. By detecting temperature changes, IRT can identify potential equipment failures, such as overheating, corrosion, and insulation defects, before they cause serious damage or downtime.

In the context of power substations, IRT can be used to monitor critical equipment, such as transformers, circuit breakers, and switchgear. By conducting regular IRT inspections, technicians can identify and prioritize potential issues, and schedule maintenance activities accordingly. For example, if an IRT inspection reveals a hot spot on a transformer, maintenance can be scheduled to repair or replace the component before it fails.

IRT inspections can be conducted remotely, without the need for equipment shutdowns or physical contact. This minimizes downtime and reduces the risk of equipment damage or personal injury. The data obtained from IRT inspections can be analyzed and used to create predictive maintenance schedules, which optimize maintenance activities and minimize costs.

Overall, the use of Infrared Thermography in Predictive Maintenance of Power Substation Equipment is a reliable and cost-effective method for ensuring the reliability and safety of electrical power systems.
DOI: 10.17632/cmnh9xc6wb.1
title: Benchmark Instances of the Quadratic Minimum Spanning Tree Problem(QMSTP)

description: Benchmark instances of the quadratic minimum spanning tree problem(QMSTP) , kindly provided by Hao Jin-kao and Fu Zhang-hua. There are 7 types of instances including CP, New Soak, OP1, OP2, RAND, SCA and SS.
DOI: 10.17632/tzzyn4ffys.1
title: NORMATIVE REFERENCE SCORE FOR THE VERTICAL JUMP TEST AMONG SCHOOL GOING CHILDREN

description: Vertical jumping is a difficult human movement requiring a lot of upper and lower body motor coordination. Vertical leaping is a vital and desirable component in many sports, that is basketball and volleyball. One essential locomotor ability is the vertical leap. The Sargent test has evolved into a common assessment method for both adults and kids. Therefore, the purpose of the current study is to find out the normative reference value of vertical jump test in school going children (8 to 14 years of age) in different body composition.
DOI: 10.17632/3ybj8jpsnk.1
title: Climate Change Perceptions Scale for Health and Related Professionals and Students (CCPS-HARPS)

description: Dataset and Supplementary Material
DOI: 10.17632/rfjb6tkzr8.1
title: WimPyDD: An object–oriented Python code for the calculation of WIMP direct detection signals

description: We introduce WimPyDD, a modular, object–oriented and customizable Python code that calculates accurate predictions of nuclear scattering expected rates for Weakly Interacting Massive Particle (WIMP) direct–detection experiments within the framework of Galilean–invariant non–relativistic effective theory in virtually any scenario, including inelastic scattering, an arbitrary WIMP spin and a generic WIMP velocity distribution in the Galactic halo. WimPyDD can also be used to analyze WIMP direct detection signals in halo–independent approaches where the velocity distribution is written in terms of a superposition of streams taken as free parameters. WimPyDD exploits the factorization of the three main components that enter in the calculation of direct detection signals: i) the Wilson coefficients that encode the dependence of the signals on the ultraviolet completion of the effective theory; ii) a response function that depends on the nuclear physics and on the main features of the experimental detector (acceptance, energy resolution, response to nuclear recoils); iii) a halo function that depends on the WIMP velocity distribution and that encodes the astrophysical inputs. In WimPyDD these three components are calculated and stored separately for later interpolation and combined together only as the last step of the signal evaluation procedure. This makes the phenomenological study of the direct detection scattering rate with WimPyDD transparent and fast also when the parameter space of the WIMP model has a large dimensionality.
DOI: 10.17632/4hh45vkp38.4
title: Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields

description: Weeds are undesired plants in agricultural fields that affect crop yield and quality by competing for nutrients, water, sunlight and space. Site-specific weed management (SSWM) through variable rate herbicide application and mechanical weed control have long been recommended in order to reduce the amount of herbicide and impact caused by uniform spraying. Accurate detection and classification of weeds in crop fields is a crucial first step for implementing such precise strategies. Drones are commonly used for image capturing but high wind pressure and different drone settings have a severe effect on the image quality, which potentially results in degraded images, e.g. due to motion blur. 
We publish a manually annotated and expert curated drone image dataset for weed detection in sorghum fields under challenging conditions. Our results show that our trained models generalize well regarding the detection of weeds, even for degraded captures due to motion blur. An UNet-like architecture with ResNet-34 as feature extractor achieved an F1-score of over 89 % on a hold-out test-set. Further analysis indicate that the trained model performed well in predicting the general plant shape, while most mis-classifications appeared at borders of the plants. Beyond that, our approach can detect intra-row weeds without additional information as well as partly occluded plants in contrast to existing research.

Github link: https://github.com/grimmlab/UAVWeedSegmentation
DOI: 10.17632/phryk6jt56.1
title: Light iron isotopes in high−silica granites record the fluid evolution in magmatic−hydrothermal system

description: These files present the electron microprobe analyses of plagioclase, whole-rock chemical and isotopic compositions, modeling parameters for the Zhengga leucogranites, as well as a compilation of Fe and Mg isotopes for mafic, intermediate and felsic igneous rocks worldwide.
DOI: 10.17632/h3rswz6hz5.1
title: Bibliometric data files (WOS_source)

description: The data files included are derived from the Web of Science search engine. The bibliographic database files were exported as Tab-delimited files-(Mac-OS or Win-dows) that contain “Full Record” and “Full Record and Cited References” files. The de-rived information from these full record files included the following: authors, titles, year of publication, keywords, subject categories, journals, organizations, and the number of citations for each article.
DOI: 10.17632/mcvdvvs43x.1
title: Vocalization associated respiration patterns: thermography-based monitoring and a potential for detecting preparation for calling 

description: Sample data and script for detecting respiration cycles and phases based on continuous temperature reading of the nasal area.
DOI: 10.17632/bpt2hvf8n3.4
title: Multi-Keyboard Acoustic (MKA) Datasets

description: Our research team from the Computer Science Department at the University of Halabja has developed an innovative dataset collection named the Multi-Keyboard Acoustic (MKA) Datasets. The Multi-Keyboard Acoustic (MKA) Datasets, designed to aid in keyboard sound recognition and analysis, address the critical need for defending against acoustic-based cyber threats. With the increasing sophistication of cyberattacks, focusing on keyboard acoustics is particularly timely. The MKA Datasets encompass detailed recordings from six commonly used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. Each platform's dataset includes raw recordings, segmented sound files, and matrices derived from these sounds, capturing the subtle variations in typing behavior across different devices and applications.
 
We meticulously organize the MKA datasets to facilitate ease of use and thorough analysis. Each platform has a dedicated folder containing subfolders for raw data, segmented sound files, and matrices. Additionally, an aggregated folder combines data from all platforms, providing a broad spectrum for cross-platform analysis. In total, the MKA datasets consist of around 2630 files with.wav extensions for sound segments, as well as an equal number of matrix and.txt files. The number of files varies by platform, with approximately 70 files for HP, Lenovo, MSI, Zoom, and Messenger, and 61 files for Mac. Within each platform's dataset, the "Sound segments" folder stores six one-second WAV audio excerpts derived from the corresponding raw data files for each class, renamed using a convention of "class_name+1" to "class_name+6" for each platform individually and "class_name+platform_name1" to "class_name+platform_name6" for the aggregated datasets. The "Sound segment (.matrix)" folder contains feature representations, such as MFCCs, extracted from each sound segment. Additionally, the "Sound segment metadata (.txt)" folder holds detailed information for each sound segment, including recording conditions, platform information, and keystroke class labels.
 
Beyond cybersecurity, the MKA datasets have potential applications in domains such as speech recognition and natural language processing. The datasets, which provide a diverse set of sound profiles, support the development of more robust and adaptable algorithms in these fields. The versatility of the MKA datasets makes them an invaluable tool not only for advancing cybersecurity research, but also for improving the efficiency and accuracy of human-computer interaction technologies. Through our comprehensive approach, we aim to contribute significantly to both academic research and practical applications in these interconnected areas.
DOI: 10.17632/jzfs2zt89t.1
title: The Emerging International Trade in Hydrogen: Environmental Policies, Innovation, and Trade Dynamics

description: This dataset accompanies the research paper "The Emerging International Trade in Hydrogen: Environmental Policies, Innovation, and Trade Dynamics" in the Journal of Environmental Economics and Management (2024). The dataset contains the results of all of our scenarios and simulations, with accompanying graphs. The dataset also contains the C++ code for the simulation along with documentation.
DOI: 10.17632/k55rb6zk3v.1
title: Demographics of Upper-Middle Class Citizens in Gachibowli, Hyderabad, India

description: This dataset is one which highlights the demographics of Upper-Middle Class people living in Gachibowli, Hyderabad, India and attempts to, through various methods of statistical analysis, establish a relationship between several of these demographic details.
DOI: 10.17632/5thd2k26sz.1
title: Global Ski Resort Rankings Dataset

description: A comprehensive dataset containing crowdsourced rankings of nearly all ski resorts worldwide. The dataset includes detailed information on each resort, such as location, snowfall, number of lifts and slopes, total slope length, and vertical drop. The dataset is updated regularly as more votes are collected.
DOI: 10.15468/dl.ua64vj
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-102.75724 10.66371,-83.24276 10.66371,-83.24276 24.33629,-102.75724 24.33629,-102.75724 10.66371))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Olmeca Soderstr."
 ]
}

 The dataset includes 240 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009374-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7dxg9n2t6w.1
title: Papaya Fruit Diseases

description: Four papaya diseases for classification included in four folders
DOI: 10.17632/c6jxmw3z4k.1
title: East African Community Public debt and economic growth data set

description: This dataset contains data for seven variables for six East African Community Countries. These variable are GDP growth per capita, public debt as a percentage of GDP, foreign direct investment inflow, taxation revenue, liquid liabilities, gross domestic savings and economic freedom as a measure of institutional quality. The data is presented as natural log.
DOI: 10.17632/vsjtbzjttb.4
title: Electrical demand/consumption and climatological data of a small Mexican community

description: The present dataset contains electricity demand/consumption and meteorological data measured in a small community within the Mexican State of Puebla. The data is contained in two separate folders named electrical_demand_consumption and weather. The first one contains electricity demand (i.e. kW) data in 15-minute resolution, as well as data regarding the hourly and daily electricity consumption (i.e. kWh) for five different households. The data is contained within .csv files that have the following name convention: houseteh(household number) _(resolution).csv. All data was measured from May 2022 to May 2023 and is represented as a time series. In other words, each measurement is linked to a specific time, which is given using the format dd/mm/yyyy HH:MM and is contained in the first column of every .csv file. Please note that missing values are represented with a –1; these values were added by the authors to facilitate the identification of missing values. The second folder contains---as the name suggests—the weather measured within the community. This data is contained in two separate .csv files. The first one (weather_outdoor variables.csv) contains the outdoor variables: Outdoor Temperature (°C), Feels Like (°C), Dew Point (°C), Wind Speed (m/s), Wind Gust (m/s), Max Daily Gust (m/s), Wind Direction (°), Hourly Rain (mm/h), Event Rain (mm), Daily Rain (mm), Weekly Rain (mm), Monthly Rain (mm), Yearly Rain (mm), Absolute Pressure (hPa), Relative Pressure (hPa), Humidity (%), Ultra-Violet Radiation Index, Solar Radiation (W/m^2), Avg Wind Direction (10 min) (°), Avg Wind Speed (10 min) (m/s). The second one (weather_indoor variables.csv) contains indoor measurements: Indoor Temperature (°C), Indoor Humidity (%), Indoor Feels Like (°C), and Indoor Dew Point (°C). All data was measured from May 2022 to March 2023, with an irregular time interval sampling, using the format dd/mm/yyyy HH:MM in the first column of the .csv file. For these files, missing values are not included.
DOI: 10.15468/dl.swx22b
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ptychosperma elegans (R.Br.) Blume",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 275 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009375-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6nv7gt2636.1
title: Data for: Identification of plant leaf diseases based on deep transfer learning

description: Experimental data & computer program.
DOI: 10.17632/cpm7y44746.1
title:  Dataset for Adulterated Red Chilli Powder with Brick Powder

description: It contains high-quality images of Red Chilli Powder adulteration with Red Brick Powder at 12 different proportions:- 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% adulterant.
DOI: 10.17632/75m8vz7jr2.4
title: Tennis Shot Side-View and Top-View Data Set for Player Analysis in Tennis

description: The dataset contains 472 clips (both of side-view and top-view are 236 clips), and 4 records file. 

We classify to two main directories: Outdoor field (tennis clay court) and Indoor field (build in stadiums self), planning two type shot: straight shot and cross-court shot. Then, recording the side-view and top-view videos of the player hit ball's process, and cut the videos to clips of each ball hit by player. 

After finish tennis’ shot, we record each ball’s real landing point coordinates. These data are recorded in the CSV files, and the CSV files is under the root folder. The file’s name indicates the data is from where: field categories and shot types. The records are represented by distance of the closest doubles sideline and the distance of the baseline (outside ball represent by negative number).