cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sdvtd7tbb9.2
title: Calculation of the CIELAB color coordinates and differences for map color legends

description: This MS Excel spreadsheet implements the procedure for calculating the CIELAB perceptually-uniform color attributes and color differences from the primary-defined RGB color coordinates, which are integer numbers between 0 (zero) and 255. It may be used by map designers to obtain specific color legends according to their requirements. The spreadsheet contains a starting list of colors (the Romanian color standard for soil type map legends), which is color-ordered (one color in a row; approximate spectral-order, including lightness/chroma degrees). The CIELAB color attributes, color attribute differences and overall color difference are those defined by the CIELAB color model/space (CIE, 2007). This model/space represents a color by using three abstract coordinates (orthogonal axes): L* has values between 0 (black) and 100 (white), indicating the color lightness; a* has positive values indicating amounts of red, negative values indicating the amounts of green and the value zero indicating neutral grey; b* has positive values indicating amounts of yellow, negative values indicating the amounts of blue and the value zero indicating neutral grey. From these three coordinates, other important perceptually-uniform color attributes are calculated: CIELAB chroma (C*ab), CIELAB hue angle/angle° (hab/hab°), CIELAB attribute differences (DL*, Da*, Db*, DC*ab, Dhab) and overall CIELAB color difference (DE*ab). The color differences calculated regarding a color are those between that color and the immediately-preceding color in the list. The list being color-ordered, the adjacent colors are normally the closest colors in the list, thus the color differences between them may be easily checked. Different other colors that appear as close in the list may be duplicated near others to see the color differences between them. The implemented calculation procedure consists of the following steps: (i) transformation of RGB coordinates to CIEXYZ coordinates (tristimulus values) (IEC, 1999), (ii) transformation of CIEXYZ coordinates to CIELAB (perceptually-uniform) coordinates and calculation of other CIELAB color attributes (CIE, 2007), and (iii) calculation of CIELAB colour differences (perceptually-uniform) (CIE, 2007). References: Brychtova, A., Coltekin, A., 2017. Calculating colour distance on choropleth maps with sequential colours – A case study with ColorBrewer 2.0. Kartographische Nachrichten 2, 53-60. CIE, 2007. CIE S 014-4:2007. Colorimetry - Part 4: CIE 1976 L*a*b* Colour space. Commission Internationale de l'Eclairage / International Commission on Illumination, CIE Tech. Committee 1-57, ISO 11664-4:2008. IEC, 1999. IEC 61966-2-1. Multimedia systems and equipment - Colour measurement and management. Part 2-1: Colour management – Default RGB colour space – sRGB. International Electrotechnical Commission, IEC Technical Committee 100.
DOI: 10.17632/43d75vptz4.2
title: Jamun Leaf Disease Detection

description: Description The Jamun Leaf Disease Detection Dataset is a comprehensive collection of high-quality images of jamun leaves, designed to facilitate the development of robust jamun leaf disease detection systems using deep learning models. The dataset was meticulously gathered from two prominent areas in Cumilla, Bangladesh: Titas and Barura. It encompasses a diverse range of jamun leaf conditions encountered in real-world jamun cultivation, ensuring its practical relevance. Dataset Characteristics Origin: Titas and Barura, Cumilla, Bangladesh Image Type: Color photographs Image Resolution: Varies Number of Images: Adequate for training and testing deep learning models Number of Classes: 6 Bacterial_Spot Brown Blight Dry Healthy Powdery Mildew Sooty Mold Usage The Jamun Leaf Disease Detection Dataset is primarily intended for developing and evaluating deep learning models for jamun leaf disease detection. It can also be used for various research purposes, such as analyzing the prevalence of different jamun leaf diseases and investigating the impact of environmental factors on leaf health. Significance The Jamun Leaf Disease Detection Dataset is a valuable resource for advancing the field of precision agriculture in Bangladesh. By enabling the development of accurate jamun leaf disease detection systems, it has the potential to significantly enhance jamun cultivation practices, leading to increased productivity and reduced economic losses.
DOI: 10.17632/p8hvpw6836.1
title: evaluative devices data for Mandarin-speaking children aged 3 to 6

description: This data is about the evaluative devices employed by Mandarin-speaking children aged 3 to 6 in their personal narratives.
DOI: 10.17632/2gt9nsj2py
title: Supplementary FASTA file 1. Multiple amino acid sequence alignment of concatenated sequences of a conserved set of proteins encoded in 33 GPA BGC from Pseudonocardiales spp.

description: Supplementary FASTA file 1. Multiple amino acid sequence alignment of concatenated sequences of a conserved set of proteins encoded in 33 GPA BGC from Pseudonocardiales spp., including orthologues of: Tba, OxyA, OxyB, OxyC, Bhp, BpsD, HmaS, DpgA, DpgB, and DpgC coded in bal.
DOI: 10.17632/fr48bmnhxj.1
title: Acoustic cognitive map-based navigation in echolocating bats

description: Data for Acoustic cognitive map-based navigation in echolocating bats
DOI: 10.17632/rzjww3vdxt.3
title: Advanced Dataset on Money Plant Diseases for AI Pathology Research

description: 1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (596 images), Bacterial Wilt Disease (576 images), and Healthy (700) images. These classes represent both damaged and healthy leaves. The dataset has 2100 images in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 1872 images depicting Bacterial Wilt Disease (576), Healthy (700), and Manganese Toxicity (596) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 6000 augmented images are made from these original photos to increase the quantity of data sets.
DOI: 10.17632/v8f42rn2zs.1
title: PETOOL v2.0: Parabolic Equation Toolbox with evaporation duct models and real environment data

description: A new version of PETOOL (Parabolic Equation Toolbox) is introduced with various additional capabilities. PETOOL is an open-source and MATLAB-based software tool with a user-friendly graphical user interface (GUI) for the analysis and visualization of electromagnetic wave propagation over variable terrain and through arbitrary atmosphere. Four novel features of the second version are as follows: (i) Several evaporation duct models have been developed. (ii) Real atmosphere data have been included in the form of “Binary Universal Form for Representation (BUFR)” data developed by “World Meteorological Organization (WMO)”. (iii) Real terrain data have been incorporated into the toolbox in the form of “Digital Terrain Elevation Data (DTED)” developed by “National Imagery and Mapping Agency (NIMA)”. (iv) A special add-on has been developed to generate a 3D coverage map of propagation factor/loss on real terrain data. The toolbox can be used for research and/or educational purposes to analyze more realistic propagation scenarios in an easier and flexible manner. The previous version of this program (AEJS_v1_0) may be found at https://doi.org/10.1016/j.cpc.2011.07.017.
DOI: 10.17632/wyr5nwvph7.1
title: Data for: HITRAN2016 line file with line shape parameters for the H2O-H2 collision system

description: HITRAN2016 line file with hydrogen as the broadening gas. The line file uses the new temperature dependence of the half-width and line shift by Gamache and Vispoel. Please see the read_me.txt file for details.
DOI: 10.17632/c3wfk7vw55.1
title: Data for: Development of Constitutive Material Model of 3D Printed Structure via FDM

description: DatainBrief
DOI: 10.17632/b6fftwbr2v.3
title: FruitNet: Indian Fruits Dataset with quality (Good, Bad & Mixed quality)

description: High quality images of fruits are required to solve fruit classification and recognition problem. To build the machine learning models, neat and clean dataset is the elementary requirement. With this objective we have created the dataset of six popular Indian fruits named as “FruitNet”. This dataset consists of 14700+ high-quality images of 6 different classes of fruits in the processed format. The images are divided into 3 sub-folders 1) Good quality fruits 2) Bad quality fruits and 3) Mixed quality fruits. Each sub-folder contains the 6 fruits images i.e. apple, banana, guava, lime, orange, and pomegranate. Mobile phone with a high-end resolution camera was used to capture the images. The images were taken at the different backgrounds and in different lighting conditions. The proposed dataset can be used for training, testing and validation of fruit classification or reorganization model. 

[The related article is available at: https://www.sciencedirect.com/science/article/pii/S2352340921009616. Cite the article as : V. Meshram, K. Patil, FruitNet: Indian fruits image dataset with quality for machine learning applications, Data in Brief, Volume 40,
2022, 107686, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2021.107686 ]
DOI: 10.17632/d76mknr6sm.1
title: Techno-eustress in HETs - original data

description: Survey and original data June 2020 for SPSS and AMOS analysis
DOI: 10.17632/gmv39p9zw9.1
title: RWLC-7 Data 2024

description: RWLC-7 Metal Organic Frameowrk Photophysical Data
DOI: 10.17632/kg8v7jrgg7.1
title: Cloud-Based Real-Time Collaboration (CRTC) in Construction Planning Phase: Adoption, Performance, and Success Factors Explored

description: The construction industry is confronted with persistent challenges in the enhancement of project planning and collaboration to enhance efficiency and performance. Nevertheless, recent technological advancements have introduced cloud-based real-time collaboration (CRTC) tools as promising solutions for streamlining project planning processes. The objectives of this research are (1) to investigate the current adoption rate and tools of CRTC in the construction industry within the project planning phase, (2) to identify the performance rate of construction projects that adopted CRTC, and (3) to analyse the critical success factors that contribute to the adoption of CRTC in the construction industry. The quantitative method was employed, utilising a structured questionnaire survey to gather data from 120 respondents who represent various construction industry players. The research findings highlighted that the current adoption rate is "moderately familiar" with CRTC in the construction industry, and the performance rate of construction projects is "significantly better" when CRTC is adopted. Five (5) key factors impact the successful adoption of CRTC tools into construction project planning processes: (1) top management support; (2) budget and cost; (3) data security and privacy; (4) compatibility; and (5) perceived industry pressure. By comprehending and identifying these factors, construction stakeholders can develop targeted strategies to effectively expedite the adoption of CRTC tools. Furthermore, this research contributes to the expanding body of knowledge on digital transformation within the construction industry and provides valuable insights for decision-makers, project teams, and technology providers. The research was aligned with the Construction 4.0 Strategic Plan (2021–2025), Malaysian Digital Economic Blueprint, and Sustainable Development Goals (SDGs) No. 9. Additionally, the outcomes of this research have the potential to cultivate a collaborative environment within the construction industry, resulting in improved project planning and execution, reduced project delays, and enhanced overall project outcomes.
DOI: 10.17632/j36zgxnc5p.2
title: Instances of "Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty"

description: This data set concerns the experimental instances of the paper "Humanitarian relief distribution with trucks and drones under travel time uncertainty".

We adapt the R101, C101, and RC101 instances of Solomon (1987), which are used to benchmark vehicle routing problems with time windows, with locations randomly scattered over the area (R), clustered in groups of locations (C), and locations both clustered in groups as well as randomly distributed over the area (RC). Besides the Solomon inspired cases, we evaluate two historical disaster cases, namely the 2015 earthquake in Nepal and 2018 tsunami in Indonesia.  The level of uncertainty within an area can differ, e.g., parts close to the path of a hurricane or near the epicenters of earthquake aftershocks are likely to exhibit larger variance in travel time. We created risk matrices for all node pairs.

The real-world cases we consider the disaster areas after the 2015 earthquake in Nepal and the 2018 tsunami in Indonesia. In 2015, a 7.8M earthquake caused major disruption in Nepal. Eleven districts near the capital city Kathmandu were affected the most, where the mountainous surroundings posed challenges to the road transport. We focus on two districts that were significantly disrupted: Nuwakot and Dhading. Based on data collected from a field trip in August 2021 (carried out as part of our research) along multiple municipalities in the affected districts, we composed a set of 84 demand nodes and a depot located in Dhading Besi, the district headquarter of the Dhading district. We marked risk levels for all node pairs, with high risks close to the epicenter, and medium risk nodes up in the mountains. In 2018, an 7.5M undersea earthquake and a tsunami that followed resulted in a massive disruption of the city Palu and surrounding areas around the Palu Bay on the Indonesian island Sulawesi. Data from a displacement tracking matrix (International Organization for Migration 2019) with locally collected data from 2018 allows us to extract a set of 343 demand nodes and a depot at the Palu Airport. We marked the risk levels along the coast line and medium risk levels in areas that experienced soil liquefaction.

For our experiments, we consider two fleet configurations. One fleet with only trucks, and one fleet where 50% of the trucks are replaced by drones. For the instances with 26 nodes, we deploy two vehicles, with the 101 node instances eight vehicles, with the Nepal case six vehicles, and with the Indonesia case 16 vehicles considering the large number of nodes. We assume that a truck has a capacity of 10 units. Considering demands between one and five, a truck can visit at least two and at most 10 nodes. The specifications of the drone are inspired by the MiniFreighter from Wings For Aid. For the experiments, we assume that drones carry one unit of demand, and travel with a constant speed of 120 km/h following Euclidean distances, as their flights are not disrupted by damaged infrastructure.
DOI: 10.17632/w5nfsc5pwy.1
title: Dataset for Systematic Review of Technological Innovation in Peanut Production

description: Background: The peanut production chain has played an important role in the production of food in a sustainable way and positively impacting economic indicators, thanks to the development of technologies that enable advances in the sector both downstream and upstream. Being extremely important a survey on the types of technological innovation adopted or developed to improve the performance of the results and corroborate for the sustainable development. Theme: The review aims to identify answers to the question: How does technological innovation contribute to the development of the peanut production system? Given the importance of technological innovation and its possible contributions to the peanut production chain, this research is justified. Objective: The general objective was: To identify how technological innovation contributes to the development of the peanut production system. Materials and methods: The methodology applied is of a basic nature by means of an exploratory theoretical essay with a qualitative and quantitative approach through a Systematic Literature Review structured in a state-of-the-art formal protocol. With the help of the StArt software to read and manage the selection and extraction processes. Being meta-analytical of the data, results identified with the use of the software and Iramuteq in the analysis of content based on statistics and ROB2 in the risk of bias assessments. Descriptive and analytical in summarizing the report of discoveries. Result Expectation: It is expected to identify relevant information for reports that add value in the construction of knowledge and scientific development regarding technological innovation and contribution to sustainable development for peanut production.

Key words: Peanut; Production chain; Sustainable development; Innovation; Technology.

Data Value
The data are useful for the validation of the research, to help other researchers who are working on studies related to the theme, allowing the continuation of new studies and corroborating the state of the art.
They can benefit the academic community, technology research and development institutes, agribusiness and food engineering.
Can be used partially and or fully in the development of new studies, being mandatory the citation and reference of this dataset.

Objective
The research that guided the generation and structuring of the present data sought, through the Formal Survey of the state of the art through a Systematic Review - RS, comprising the period from 2016 to 2021, the objective of:
Identify how technological innovation contributes to the development of the peanut production system.
a)	The areas (production, distribution, processing, commercialization, etc.) that have been innovated;
b)	The results observed with technological innovation regarding applications and usability;
c)	How technological innovations contribute to sustainable development.
DOI: 10.17632/w7nw4km7xd.1
title: Spatial Multimodal Analysis of Transcriptomes and Metabolomes in Tissues

description: We present a spatial omics approach that combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinson’s disease.
DOI: 10.17632/bx2mtwzcnx.1
title: Data on celebrity image influencing buyers’ behaviour

description: This data set examines if the public image of the endorsing celebrity to a product influences the buying actions of residents in some urban, suburban and rural locations in Lagos and Ogun States, South Western Nigeria. The survey was carried out among some residents in the urban areas of Victoria Island, Surulere and Ikeja; the suburban areas of Oko Oba, Olorunsogo, Abeokuta and Ogba and the rural communities of Ikotun, Iyesi, Egbeda, Atan and Ilugun. Copies of the questionnaire were administered to 1534 respondents from these areas. The data indicates that the public image of the celebrity actually has influence on whether buyers choose to buy the product he is endorsing. However, the buyers say that they would not stop buying a product already endorsed by a celebrity even if he is to run into scandals.
DOI: 10.17632/7jnyjdbvf9.1
title: CRM: Common Research Model

description: These files contain the baseline and optimized geometries and grid files of NASA's Common Research Model (CRM) for the AIAA Aerodynamic Design Optimization Discussion Group's (ADODG) wing optimization benchmark (case 4). Details about the models and methods used can be found in reference [1]. The flight conditions are: Mach number = 0.85 Reynolds number = 5.0 million The baseline zip file includes the baseline CRM geometry in the following formats: - IGS - CGNS, multiblock-structured, 28M grid - CGNS, multiblock-structured, 3M grid - CGNS, singleblock-unstructured, 3M grid - CGNS, multiblock-structured, 450k grid - CGNS, singleblock-unstructured, 450k grid The optimized zip file includes the optimized geometry in the following formats: - PLOT3D, 28.8M grid - CGNS, multiblock-structured, 28M grid - CGNS, multiblock-structured, 3M grid References: 1. Lyu Z, Kenway GKW, Martins JRRA. Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark. AIAA Journal. 2015 ;53(4):968–985.
DOI: 10.17632/7r4z3p3g4m.1
title: N&C-TEC: ECG and EEG files

description: Electroencephalogram (EEG) and electrocardiogram (ECG) data in MAT format from 16 subjects. For the study, sixteen (nine men and seven women) undergraduate students were recruited voluntarily. They were between 19 and 25 years old. They were divided into four groups, and two collaborative activities were undertaken with and without noise. EEG and ECG baseline status of each subject was recorded for 3 minutes, the second collaborative activity (without noise) was recorded for 10 minutes and the last collaborative activity (with noise) was also recorded for 10 minutes ECG signals. In this study, with the BIOPAC system, the electrical activity of the heart was recorded at 200 Hz within a bandwidth of 0.1 to 100 Hz using the Einthoven Ⅰ triangle lead. EEG Signals. The recording sites of the MUSE headband were mounted according to the 10/20 International System, and four EEG channels were recorded at 220 Hz within a bandwidth from 0.1 to 100 Hz. The EEG channels were: AF7, AF8, TP7 and TP8.