cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fs7mtkg2wk.5
title: Dataset of Deep Learning from Landsat-8 Satellite Images for Estimating Burned Areas in Indonesia

description: The dataset consist of three categories; image subsets, burned area masks and quicklooks. The image subsets are derived from Landsat-8 scenes taken during the years 2019 and 2021. Each image has a size of 512x512 pixels and consists of 8 multispectral. The sequence of band names from band 1 to band 7 of the image subset is same as the sequence of band names of landsat-8 scene, except for band 8 of the image subset which is band 9 (cirrus band) in the original landsat-8 scene. The image subsets are saved in GeoTIFF file format with the latitude longitude coordinate system and WGS 1984 as the datum. The spatial resolution of image subsets is 0.00025 degree and the pixel values are stored in 16 bit unsigned integer with the range of value from 0 to 65535. The total of the dataset is 227 images which containing object of burned area surrounded by various ecological diversity backgrounds such as forest, shrub, grassland, waterbody, bare land, settlement, cloud and cloud shadow. In some cases, there are some image subsets with the burned areas covered by smoke due to the fire is still active. Some image subsets also overlap each other to cover the area of burned scar which the area is too large.
The burned area mask is a binary annotation image which consists of two classes; burned area as the foreground and non-burned area as the background. These binary images are saved in 8 bit unsigned integer where the burned area is indicated by the pixel value of 1, whereas the non-burned area is indicated by 0. The burned area masks in this dataset contain only burned scars and are not contaminated with thick clouds, shadows, and vegetation. Among 227 images, 206 images contain burned areas whereas 21 images contain only background. The highest number of images in this dataset is dominated by images with coverage percentage of burned area between 0 and 10 percent.
Our dataset also provides quicklook image as a quick preview of image subset. It offers a fast and full size preview of image subset without opening the file using any GIS software. The quicklook images can also be used for training and evaluating the model as a substitute of image subsets. The image size is 512x512 pixels same as the size of image subset and annotation image. It consists of three bands as a false color composite quicklook images, with combination of band 7 (SWIR-2), band 5 (NIR), and band 4 (red). These RGB composite images have been performed contrast stretching to enhance the images visualizations. The quicklook images are stored in GeoTIFF file format with 8 bit unsigned integer.

This work was financed by Riset Inovatif Produktif (RISPRO) fund through Prioritas Riset Nasional (PRN) project, grant no. 255/E1/PRN/2020 for 2020 - 2021 contract period.
DOI: 10.17632/jkjvmn9tz3.1
title: Wind Turbine Accident News (1980-2013)

description: This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S. and Ulun, S., 2017. Wind turbine accidents: A data mining study. IEEE Systems Journal, 11(3), pp.1567-1578. As of now, the most extensive data available on the Internet on wind turbines accidents is published by the Caithness Windfarm Information Forum (CWIF), a UK-based grassroots organization opposing wind turbine installations. While the Caithness list is impressive in magnitude, the quality and reliability of the list is open to discussion because of the following reason: * Many of the web links to the news sources are not valid, and some of the accidents appear in multiple lines of the data. In spite of containing much more magnitude of data, the data available in other online sources also exhibit similar deficiencies. So, there are problems when it comes to using the Caithness data or other data in research studies. To this end, we collected data on wind turbine accidents ourselves, also using the data from Caithness and we share our collected data on this page (please click the link at the top of the page to download the data). The data we collected consists of three folders, and a MS Excel file. The folder News.txt contains the accident news, with each news in a separate text file: The folder News.doc contains news, with each news in a separate MS Word file: Finally, the folder News.doc.with.notes contains news, with each news in a separate MS Word file, but with extensive comments, explaining how the database in the MS Excel file was constructed: The MS Excel file News.Database.xlsx contains the structured data created based on the detailed reading of the accident news text: The MS Excel file is the file that was analyzed in our research paper.
DOI: 10.17632/pt9d2rdv46.1
title: resting-state fMRI data for Normal adults

description: Whole brine resting-state fMRI data from 227 healthy adults between 18 and 78 years old acquired at a 3T clinical MRI scanner. The tar file contains 227 compressed nifti files. The 1st alphabet in the file names indicates the gender of the volunteers (f for female and m for male). The 2nd and 3rd digits indicate the age of volunteers. The remaining alphabets are randomized to encode individual subjects. The original images were reconstructed into dicom format and converted into nifti file format.
DOI: 10.17632/pj4jxgrmmr.1
title: GRASP92: A package for large-scale relativistic atomic structure calculations

description: Abstract We describe a suite of programs for multiconfiguration or configuration-interaction relativistic atomic structure calculations with large configuration state function lists. Atomic orbitals are taken to be four-component spinors. Multiconfiguration self-consistent-field calculations are based on the Dirac-Coulomb Hamiltonian; at this level nuclei are assumed stationary and may be modelled either as point sources or as spherically-symmetric extended sources; in the latter case the radial varia... Title of program: GRASP92 Catalogue Id: ADCU_v1_0 Nature of problem Prediction of atomic spectra - atomic energy levels, oscillator strengths, and radiative decay rates - using a 'fully relativistic' approach. Versions of this program held in the CPC repository in Mendeley Data ADCU_v1_0; GRASP92; 10.1016/0010-4655(95)00136-0 ADCU_v1_1; GRASP92; 10.1016/j.cpc.2006.07.021 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/87nrnvwzkj.1
title: RhizomeSlideDataset

description: Dataset contain 4 Type in Ginger family image: 
1. Phalai
2. Tumeric
3. Wild tumeric
4. Zedoary
DOI: 10.17632/m6syhcn2nh.1
title: POA-Net: Dance-8 and Dance-20 Datasets

description: This work (POA-Net) contributes a new image dataset contains 8 dance styles representing Indian and international dance themes, named Dance-8. Combined with Dance-12 from our previous work (SYD-Net), Dance-20 is developed for research. This is a part of our recent research work at BITS Pilani, Pilani Campus, Rajasthan, India. Ojas, Parth and Aditya equally contributed in the proposed POA-Net work.
DOI: 10.17632/7j9gpjpk6t.2
title: TesTactico for F7: Construct Validity

description: Validation of “TesTactico for F7”: A tool to analyze Declarative Tactical Knowledge based on a Football Competence Observation System

Dataset (construct validity)
high-level young football players (n = 80) and low-level young football players (n = 75)
Type: .sav (SPSS)
DOI: 10.17632/cvkdzr8tv3.4
title: Global Innovation Index: panel data (2011-2022)

description: The aim of this work is to provide a panel data file with all pillars of the Global Innovation Index from 2011 until 2022, covering all available economies (149 in total) by income level.
DOI: 10.17632/kp52v9pn6k
title: ICTs_poverty_Africa

description: Data on information and communication technologies and poverty in Africa
DOI: 10.17632/gr99ny6b8p.1
title: DWSD: Dense Waste Segmentation Dataset

description: Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.
DOI: 10.15468/dl.hcugdf
title: Occurrence Download

description: A dataset containing 688 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Towson University - MidAtlantic - Ailanthus altissima - 2011"
 ]
}

 The dataset includes 688 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009248-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2fxz4px6d8.4
title: Augmented COVID-19 X-ray Images Dataset

description: This Dataset Contains augmented X-ray Images for COVID-19 for COVID-19 Disease Detection Using Chest X-Ray images. The dataset is collected from two online available datasets (https://github.com/ieee8023/covid-chestxray-dataset and https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The dataset contains two folders one for COVID-19 Augmented images while Non-COVID-19 is not augmented and the other folder contains augmented images for both COVID-19 and Non-COVID-19.
DOI: 10.17632/7vb77bz2st.2
title: Lentil Plant Disease Image Dataset (4 Class)

description: The Lentil Plant Disease Image Dataset is a meticulously collected, organized, augmented, and preprocessed collection of high-resolution images designed to support research in plant pathology and machine learning. This dataset includes images of lentil plants affected by three common diseases—Ascochyta Blight, Lentil Rust, and Powdery Mildew—as well as healthy plants. 

Captured using a Samsung M31 smartphone, the images encompass various growth stages and lighting conditions, ensuring a comprehensive representation of symptoms. Data augmentation techniques, including rotation, width/height shift, shear, zoom, horizontal flipping, and brightness adjustment, have been applied to enhance the dataset's variability and robustness.

The dataset is divided into training, testing, and validation sets with an 80-10-10 split, and it is meticulously labeled and organized. This makes it an invaluable resource for researchers and practitioners in the fields of computer science, artificial intelligence, computer vision, machine learning, deep learning, and agriculture.
DOI: 10.17632/s82j8dh4rr.1
title: Indonesian Herb Leaf Dataset 3500

description: Plants are one of the most important species on earth. Its role in the ecosystem, preventing natural disasters, and ingredients of many medicines are the reasons for protecting this species. The amount of biodiversity in plants is one of the challenges to maintaining biodiversity conservation.
Therefore, we need a way to protect biodiversity. 


The first step that can be done is to identify plant species. By knowing the identity of the plant, information about the type, origin, benefits, and methods of conservation of the plant can be known later.
Plants have several parts, such as roots, stems, fruits, flowers, and leaves. Identification of plants based on their leaves is more efficient because the leaves are almost there at all times, are easier to reach, and cause minor damage to the plant if the leaves are picked.
In this dataset, there are pictures of tropical leaves, especially those that can grow in Indonesia. Indonesia was chosen because Indonesia is one of the countries with the greatest biodiversity. Indonesia is also one of the countries that have tropical rain forests. There are ten plant species contained in this dataset, such as Averrhoa bilimbi (Blimbing Wuluh), Psidium guajava (Jambu Biji), Citrus Aurantiifolia (Jeruk Nipis), Ocimum Africanum (Kemangi), Aloe vera (Lidah Buaya), Artocarpus heterophyllus (Nangka), Pandanus Amaryllifolius (Pandan), Carica papaya (Pepaya), Apium graveolens (Seledri), Piper Betle (Sirih). The total dataset is 3500 images. Each species has 350 high-resolution images. Folders are named according to names in Indonesian. Each image has a white background. The format for all images is .jpg. In addition, the dimension of each image is 1600 x 1200.
The Indonesian Herb Leaf Dataset (IHLD) can be used to develop plant identification models using artificial intelligence. By releasing this dataset to the public, we look forward to stimulating research and adding content to the existing leaf datasets.
DOI: 10.17632/xnzhj3x8v4.2
title: Asphalt Crack Dataset

description: 400 images of Asphalt cracks and Non-crack images that are captured using cameras, can be used for training crack detecting models.
DOI: 10.17632/3hx58k232n.4
title: EEG/EOG/EMG data from a cross sectional study on psychophysiological insomnia and normal sleep subjects

description: The data presented here were originally collected for a research project entitled “Sleep EEG spectral analysis in psycho-physiological insomnia and normal sleep subjects ”. This article describes data of 60 subjects referred to Sleep Disorders Research Center (SDRC). The study was conducted in psycho-physiological insomnia and normal subjects. The data includes 8 EEG, 6 EOG and 3 EMG channels by 256 Hz sampling rate. Also, it includes power spectral features of each band (δ, θ, α, β) in segment of 30 s for each channel and nonlinear analysis parameters. Moreover, the complete demographic and polysomnography specifications is attached.
DOI: 10.17632/dfsf6n6g7y.1
title: Data for: Crystal structure classification in ABO3 perovskites via machine learning

description: 1. List of all 5329 ABO3 perovskite-type oxides (Microsoft Excel Worksheet (.xlsx)) 2. List of all 675 ABO3 perovskite-type oxides used in this study (Microsoft Excel Worksheet (.xlsx))
DOI: 10.17632/j6jyjnwnb7.1
title: The Virgin enthroned with Child. Tree-ring data

description: The tree-ring data refer to the panel titled The Virgin Enthroned with Child, attributed to the Flemish painter Master of Frankfurt. The panel painting measures 64.2 × 44.5 × ~0.5 cm. The upper part of the painted panel was cleaned with the help of a scalpel, while the growth rings were accentuated with a plaster-and-water paste. Sampling was conducted using a portable dendrochronograph equipped with a high-resolution digital camera. This instrument facilitated the measurement of tree rings in situ and provided a completely noninvasive approach, allowing immediate assessment of sampling quality. Data are in two formats: Heidelberg (.fh) and Tucson (.rwl).
DOI: 10.17632/fs455tz88y.1
title: Labeled dataset for despeckling SAR imagery

description: These data are according to the paper "Design of a labeled dataset for despeckling SAR imagery" publised in EUSAR2024 (European Conference on Synthetic Aperture Radar).

GT1 contains images according to Ground Truth #1
GT16 contains images according to Ground Truth #16
Noisy contains actual SAR images
DOI: 10.17632/6pwd2s9rff.2
title: The liver x receptor is selectively modulated by different ligands to differentially alter breast cancer metastasis-associated myeloid immune cells - Supplemental Data

description: This is supplemental data corresponding to the manuscript titled The liver x receptor is selectively modulated by different ligands to differentially alter breast cancer metastasis-associated myeloid immune cells.

Authors: 
Liqian Ma1, Hashni Epa Vidana Gamage1, Srishti Tiwari1, Chaeyeon Han1, Madeline A Henn1, Natalia Krawczynska1, Payam Dibaeinia2, Graeme J. Koelwyn3,†, Anasuya Das Gupta1, Rafael Ovidio Bautista Rivas1,  Chris L. Wright4, Fangxiu Xu4, Kathryn J. Moore3,5, Saurabh Sinha2,6,7 and Erik R. Nelson1,6-10,*

Affiliations:
1 Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL
2 Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL
3 NYU Cardiovascular Research Center, Leon H. Charney Division of Cardiology, Department of Medicine, New York University School of Medicine, New York, NY
4 Roy J. Carver Biotechnology Center DNA Services, University of Illinois Urbana-Champaign, Urbana, IL
5 Department of Cell Biology, New York University School of Medicine, New York, NY
6 Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, IL
7 Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL
8 Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Urbana, IL
9 University of Illinois Cancer Center, University of Illinois at Chicago, Chicago, IL
10 Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL



*To whom correspondence and reprint requests should be addressed: Erik R. Nelson. University of Illinois at Urbana-Champaign. 407 S Goodwin Ave (MC-114), Urbana, IL, 61801. Phone: 217-244-5477. Fax: 217-333-1133. ORCiD: 0000-0002-8887-1905. E-mail: enels@illinois.edu
DOI: 10.17632/852r5dnzd5.1
title: A hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources

description: The dataset contains eight spreadsheets presenting the seasonal growth of the apple tree (Malus domestica, Fuji Variety grafted on M9 rootstock) organs growing according to three conditions of carbon assimilate availability: trees in normal field conditions (FRU); trees in which fruit competition for carbohydrates was minimized by heavy thinning (THI); trees in which vegetative competition was minimized by complete fruit removal (DEF). Trees are grown in the Trentino Alto Adige apple production area, Northern Italy. The dataset includes information on primary and secondary shoot growth, secondary trunk growth, fruit growth. It includes  monitoring of tagged plant organs and a concurrent characterization of the same organ types sampled from other trees following the same treatments. Observations contain information useful for hierarchical data treatment, such as tree, branch and shoot numbers or canopy level (Low, Middle, High), as well as information such as treatment, sampling date, the day of the year of the measurements, and the corresponding daily accumulated growing degree days (GDD), calculated from air temperature.
DOI: 10.15468/dl.bbs32t
title: Occurrence Download

description: A dataset containing 913 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus strobiformis Engelm.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 913 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009249-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/395y2ps54b.1
title: Demystifying the supererogation of high-tech export competitiveness in the transition to a low-carbon economy

description: HTE competitiveness initializes the process of technology spillover and increases the pace of technological advancements. Undoubtedly, the generated technological dissemination and ongoing heavy investments in research and development yield implications about reflecting these updates to the fight against environmental degradation. Therefore, we observe the environmental consequences of HTE competitiveness and treat the induced technological developments as essential in pursuing environmental sustainability. Notably, this study has focused on this crucial interplay in G20 countries and has revealed precious implications due to the function of HTE competitiveness on sustainability. Nonetheless, the roles of affluence, technology, and trade globalization have been unveiled as moderators in this nexus.
Many essential takeaways may emerge as a result of our research within the framework of G20 countries. Firstly, HTE competitiveness has been found to undertake a pivotal role in facilitating energy consumption rationalization and other technological advances that enhance environmental quality. This insight can underscore the fact that countries that improve their HTE market share tend to manage supply chain sustainability and accomplish industrial structure supererogation on green innovation. Last but not least, strategies targeting the improvement of HTE competitiveness may catalyze the transition to a low-carbon economy and enable new emissions trading schemes to be established by providing convenient infrastructure abilities and awareness of sustainability issues.
DOI: 10.17632/xknhpz5t96.1
title: Raw dataset with accelerometer, gyroscope and magnetometer data for activities with motion

description: This raw dataset presents the data related to the walking, running, standing, walking upstairs, and walking downstairs captured with accelerometer, gyroscope and magnetometer sensors available in off-the-shelf mobile devices.
DOI: 10.17632/ynb3yf2m6h.1
title: Fish feeding intensity dataset 2

description: This is a dataset for evaluating the feeding intensity of fish. An audio-visual-acoustic dataset, MRS-FFIA, was built.This folder contains only the visual datasets, and the folder name is GRB.
DOI: 10.17632/bkxj8z84m9.3
title: Oil and Gas Tank Dataset

description: Tank Detection and Count Dataset contains 760 satellite image tiles of size 512*512 pixels and one-pixel cover 30cm*30cm at ground level. Each tile is associated with .xml and .txt files. Both .xml and .txt file contains the same annotations of oil/gas tanks but in a different format. .xml contains the pascal VOC format and in .txt file, every line contains the class of the tank and four coordinates of the bounding box: xmin, ymin, xmax, ymax.