cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/g6rbmc2ggc.1
title: Predictive Maintenance for Electrical Wiring Faults

description: Electrical systems in aviation are responsible for the effective and safe operation of essential systems of modern aircraft. Aviation maintenance personnel routinely inspect and maintain these systems to guarantee their integrity and reliability. Currently, aircraft inspections are performed manually by maintenance personnel with a flashlight, a mirror, and a notebook. This manual inspection method comes with its disadvantages, such as high worker hours and dependence on the inspector’s expertise to correctly identify and assess all discrepancies they come across. This is a dataset to support automatic optical inspection tool for electrical components using computer vision. 

This dataset was built from scratch and hand-labeled. This dataset includes images of a single computer case with multiple configurations of two power supply units, two cooling components, and four SATA cables with several wiring configurations, including various induced faults. Images were collected using a Canon S95 PowerShot camera mounted on a tripod in a windowless room with controlled lighting. 
Images were labeled in accordance with FAA best practices by a veteran aviation electrician trained in the FAA's EWIS standards using Intel’s CVAT tool. Localization was provided in the images using bounding boxes, allowing the use of existing object detection tools. Class labels were identified based on documented faults, best practices, and the expert opinion of maintenance personnel. Fault labels included misrouted cables/wires, damaged cables/wires, disconnected plugs and disconnected jacks. Faults were intentionally introduced during data collection by misrouting, unplugging, or damaging cables/wires.
DOI: 10.17632/5czndsmdmp.2
title: Environmental inequality in European NUTS regions

description: The dataset provides the population-weighted Gini coefficient and average of exposure to environmental hazards and services in European NUTS regions.
DOI: 10.17632/5tmsftb9dk.2
title: Dataset for Green consumption behaviour amongst Malaysian consumers

description: The data consists of 375 samples of data for seven variables related to green consumption behaviour - 1. Demographic information such as age, gender, education level, occupation, ethnicity, marital status, personal income, number of household and work category. 2. Media consumption preference 3. Environmental concern 4. Social influence 5. Perceived behavioural control 6. Consumer innovativeness – Novelty seeking 7. Green consumption behaviour
DOI: 10.17632/vrg53tc5r2.1
title: Science and Engineering Indicators 2024 support material

description: This repository includes the base notebooks used to prepare the patent and trademark data for SEI 2024. This covers the uploading of the PatentsView database, its curation, and the preparation of patent and trademark indicators across all the mapping classifications.
DOI: 10.17632/9m2dcb5mmr.3
title: Indigenous Dataset for Apple Leaf Disease Detection and Classification

description: This dataset, suitable for training Machine Learning and Deep Learning models, has been built by collecting images from the apple cultivation fields of Jammu and Kashmir. The dataset contains 7569 images belonging to three categories viz Healthy, Alternaria and Apple-Mosaic.
DOI: 10.17632/c7fjbxcgj9.3
title: Endoscopy Artefact Detection (EAD) Dataset (includes updated 2020 version)

description: We aim to establish a first large and comprehensive dataset for "Endoscopy artefact detection". The released data is a part of Endoscopy Artefact Detection IEEE ISBI challenge. With the Endoscopy Artefact Detection Challenge (EAD), we aim to identify hindrances like saturations, motion blur, specular reflections, bubbles, imaging artefacts, contrast and instrument using revolutionary techniques in artificial intelligence. This dataset has been updated with additional class instance (blood) and annotation samples. The provided challenge is important for endoscopy video processing task in obtaining more sensible temporal information. The challenge is sub-divided into three tasks: 1) Multi-class artefact detection: Localization of bounding boxes and class labels for 8 artefact classes for given frames 2) Region segmentation: Precise boundary delineation of detected artefacts for 5 artefact classes 3) Detection generalization: Detection performance independent of specific data type and source (test only) Useful tools for this dataset: https://sharibox.github.io/EAD2019/; https://github.com/sharibox/EndoCV2020 Updated version 2020: We have added an updated dataset of EAD2019 (https://data.mendeley.com/datasets/c7fjbxcgj9/2). Please note that several frames have been included in this dataset. If you use this dataset then please cite the listed works below: 1) Ali, S., Zhou, F., Daul, C., Braden, B., Bailey, A., Realdon, S., East, J., Wagni`eres, G., Loschenov, V., Grisan, E., et al., 2019. Endoscopy artifact detection (EAD 2019) challenge dataset. arXiv preprint arXiv:1905.03209 . 2) Ali, S., Zhou, F., Bailey, A., Braden, B., East, J.E., Lu, X., Rittscher, J., 2021. A deep learning framework for quality assessment and restoration in video endoscopy. Medical Image Analysis 68, 101900. doi:https://doi.org/10.1016/j.media.2020.101900. 3) Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R.D., et al., 2020. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific reports 10, 1–15. 4) Ali, S., Dmitrieva, M., Ghatwary, N. et al. A translational pathway of deep learning methods in GastroIntestinal Endoscopy. arXiv preprint arXiv:2010.06034, 2020.
DOI: 10.17632/z4hx2v3ywc.1
title: Rice Leaf Disease Dataset

description: 242 images of Rice Blast and Brown Spot
DOI: 10.17632/w33nhh3282.1
title: A dataset for accounting, finance and economics research on US data breaches

description: The data set consists of 506 data breaches and associated characteristics that affected US listed companies over a 10-year period from April 2005 to March 2015. The data set was gathered from the Privacy Rights Clearinghouse (PRC) and then augmented with manual data collection.
DOI: 10.17632/8gf9vpkhgy.1
title: Chest X-ray dataset for lung segmentation

description: The proposed dataset has been combined from three popular lung segmentation datasets: Darwin, Montgomery, and Shenzhen. The combined data allow researchers and clinicians to gain access to a good quality dataset, a large proportion of which has been manually annotated. The combined dataset consists of 6,810 images, with corresponding binary masks of lungs with the following distribution of images between the three datasets:
•	6,106 images from the Darwin dataset;
•	139 images from the Montgomery dataset;
•	566 images from the Shenzhen dataset.

The Darwin dataset [1, 2] images include most of the heart, revealing lung opacities behind the heart, which may be relevant for assessing the severity of viral pneumonia. The lower-most part of the lungs, where visible, is defined by the extent of the diaphragm. Where present and not obstructive to the distinguishability of the lungs, the diaphragm is included up until the lower-most visible part of the lungs. A key property of this dataset is that image resolutions, sources, and orientations vary across the dataset, with the smallest image being 156x156 pixels and the largest being 5600x4700 pixels. Furthermore, we included the portable X-ray images which are of significantly lower quality as compared to standard X-rays. A key limitation of the Darwin dataset is that it does not contain lateral X-ray lung segmentations. It is worth noting that lung segmentations were performed by human annotators using Darwin's Auto-Annotate AI and then adjusted and reviewed by expert radiologists.

Both the Montgomery and Shenzhen datasets [3] were published by the United States National Library of Medicine and are made of posteroanterior chest X-ray images. These images are available to foster research in computer-aided diagnosis of pulmonary diseases with a special focus on pulmonary tuberculosis. The datasets were acquired from the Department of Health and Human Services (Maryland, USA) and Shenzhen №3 People's Hospital (Shenzhen, China). Both datasets contain normal and abnormal chest X-ray images with manifestations of tuberculosis and include associated radiologist readings.

References:
1. 	Darwin’s Auto-Annotate AI. Available: https://www.v7labs.com/automated-annotation
2. 	COVID-19 X-ray dataset. Available: https://github.com/v7labs/covid-19-xray-dataset
3. 	Jaeger S, Candemir S, Antani S, Wáng Y-XJ, Lu P-X, Thoma G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014;4: 475–477. doi:10.3978/j.issn.2223-4292.2014.11.20
DOI: 10.17632/dck59kzzjs.1
title: Data for: How faithfully the geochronological and geochemical signatures of detrital zircon, titanite, rutile and monazite record magmatic and metamorphic events? A case study from the Himalaya and Tibet

description: This section covered our appendix data which is not shown in the manuscript, Appendix 1 is the magmatic and metamorphic age of bed rock in south Tibet from literature, Appendix 2 is the whole sand ( petrography ) and heavy mineral composition from Lhasa river, Nianchu and Pumchu respectively, the other four Appendixes from 3 to 6 are detrital zircon, monazite, titanite and rutile geochemical and U-Th-Pb dating.
DOI: 10.17632/g3v9xcxjgc.1
title: A synthetic data set with new product demand and characteristics

description: This is a synthetic data set with 2000 new products. The data set consists of product characteristics, 18 demand points, and the total demand. The data set has been created in such a way that there are links (with a certain level of noise) between product characteristics and demand. This data set can serve as a benchmark for new product forecasting and uncertainty estimation of new product demand.
DOI: 10.17632/s929ty4psy.1
title: Effect of Nb and Ag in the Microstructure and Mechanical Properties of Rare Earth modified AA2219 Aluminium Alloy

description: This data is to study the Effect of Nb and Ag in the Microstructure and Mechanical Properties of Rare Earth modified AA2219 Aluminium Alloy
DOI: 10.17632/gg24hxmj7v.1
title: Code for Thyroid Explainer Model

description: This dataset contains the source code for the Thyroid Explainer Model. 
The black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Meanwhile, explainability is not only related to AI performance but also responsibility and risk in medical diagnosis.
Thyroid Explainer is an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions where its predictions are based. It offers physicians a tool to understand the basis of AI predictions and eval its reliability
DOI: 10.17632/phm6cbdyyd.2
title: Site characteristics of current wind power deployment in Austria and Denmark

description: This data set features detailed information on wind power deployment in Austria and Denmark aggregated at 1 km spatial resolution. First, we aggegrated wind power plant location data on turbine level to pixels of 1 km size. Second, we added several characteristics which describe the pixel and the site specifications. We included data on average annual wind speeds, land use, height above sea level, population density, human impact on land following the Human Footprint Index and nature conservation areas. For a detailed description of the input data sets please refer to Nitsch, F., Turkovska, O., Schmidt, J. (2019). Observation-based estimates of wind power potential: a case study for the Czech Republic. Submitted to Energy, Sustainability and Society. This data can be used to assess wind power potentials derived from observation-based data. A first case study using this data set was conducted for the Czech Republic in Nitsch et al. (2019). Funding: The authors thankfully acknowledge funding by the European Commission (reFUEL - Going global? Renewable fuel trade and social land-use restrictions in a low-carbon energy system (758149) ERC2017-STG 758149).
DOI: 10.17632/g43g8tcxt9.1
title: Data for: The Impact of Urban Transit on Nearby Startup Firms: Evidence from Hangzhou, China

description: The data set contains firm data with geo-location, which is used in our study.
DOI: 10.17632/kz2jzsv3zf.1
title: Matlab code for Visual Impact

description: Set of files to be executed in Matlab. It includes the generic method of evaluating the visual impact, and a simplified photomontage of the farm.
DOI: 10.17632/2dxrcvpd8p.1
title: Data for: PIIGS in the Euro Area. An Empirical DSGE Model.

description: time series of selected macroeconomic variables used as observables in our estimation exercise.
DOI: 10.17632/dkf65jd9fc.3
title: Data on A Goal Programming Model for Optimizing the Reverse Logistics Network of Glass Containers, and an Application in Turkey

description: Since there is no data available in the literature or through internet, we really made an effort to find real data and try to estimate non-available data through available ones, correctly. Scalar and parameters of the mathematical model in the study are estimated according to assumptions shown as below, and are given in folders. Assumptions • There are 3 source of glass containers. These are from recycle bins, HORECA and other waste recyclers, and those amounts are estimated according to some past data. • All cost parameters are foreknown which are calculated from real data, and will be shown in detail. • Cost parameters will be determined with estimated inflation rate for the future periods and revenue parameters will be determined according to government payments per ton of recycled glass. • Glass container consumption is directly proportional with the population of a town. So, for districts in which this data does not exist, the amount of glass can be collected through recycle bins is estimated according to their populations. • The glass container source from other recyclers are assumed as 10% of total amount of glass containers available through recycle bins in each period. • At the beginning of first period, 10 million Turkish Liras (TL) budget is available. Thereby, Bp parameter becomes 10,000,000 for the first period and 0 for the rest of periods. • For operational side of glass recycling facility, we have stayed in touch with a real firm named “Cam Kırığı” located in Gebze/Istanbul through telephone interviews to get data (telephone interview, March 2019). • The information about the glass manufacturer belongs to Sisecam Company, which is biggest glass manufacturer in Turkey. • Equivalent cost parameters for future periods will be adjusted according to official price increases and inflation expectations. • 1 year consists of 250 workdays. So, WD scalar becomes 250. • Distances between facilities and glass container sources are based on highway routes. • One driver and one assistant are required for each truck type 1 whereas only a driver is needed for truck type 2. So, these are summed with the required number of workers in the facility in order to find total number of personnel.
DOI: 10.17632/x7bvzv6cvr.1
title: Dataset of Breast mammography images with Masses

description: The dataset combining four breast density categories with breast mass labeled benign or malignant, there are 8 categories in our classification task. The eight categories are: (1) The category of breast density is 1 and breast mass is benign (Density1+Benign) (2) The category of breast density is 1 and breast mass is malignant (Density1+Malignant) (3) The category of breast density is 2 and breast mass is benign (Density2+Benign) (4) The category of breast density is 2 and breast mass is malignant (Density2+Malignant (5) The category of breast density is 3 and breast mass is benign (Density3+Benign) (6) The category of breast density is 3 and breast mass is malignant (Density3+Malignant) (7) The category of breast density is 4 and breast mass is benign (Density4+Benign) (8) The category of breast density is 4 and breast mass is malignant (Density4+Malignant). In addition, the data contains images before and after data augmentation, and the image matrix was 224 x 224 or 227 x 227.
DOI: 10.17632/thdfw6dz4n.1
title: Standard Operating Procedure for the Archimedes Density Method for Metal Additive Manufacturing

description: This is a Standard Operating Procedure for the Archimedes density method, commonly used to estimate the density of additively manufactured metal parts. This was developed leveraging ASTM standards, but modified based on practice and lessons learned.
DOI: 10.17632/tjhzpdkvmp.1
title: LA-ICP-MS and stable isotope data of Waihau Bay (North Island, New Zealand) carbonate veins [Dataset]

description: This dataset contains the raw LA-ICP-MS and stable isotope data of carbonate veins collected in Waihau Bay, North Island, New Zealand
DOI: 10.17632/jnjwjxgyd7.1
title: Data of L2 motivation comparison of Spanish and Japanese high school students

description: This is the data of the project which explored the motivational behaviour of L2 high school students from Spain and Japan. Time period of data collection: November 2021 – February 2022. The tool of analysis: MFQ, Likert-scale (1-5). The data collected was exported to the statistical software SPSS version 24.
DOI: 10.17632/mrcf7f4tc2.3
title: Clinical Knowledge Graph

description: We have developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Here, we are uploading a dump of CKG's database that can be used to easily create a Neo4 graph database harmonizing 9 ontologies, 26 relevant biomedical databases. Experimental studies included in the publication are also included as CKG reports. To get the full database up and running follow the installation instructions specified in CKG's documentation (https://github.com/MannLabs/CKG) and GitHub repository (https://github.com/MannLabs/CKG).
DOI: 10.17632/b7vhyd982r.1
title: Metadata about IOT in Agriculture and Climate Change

description: Search made at Web of Science on September, 2023.

Search query:  

item Topic: "IoT"
Topic: "agricult* OR agribusi*"
Citation Topics Meso: 6.153 Climate Change
DOI: 10.17632/pr7bgzxpgv.1
title: Spectrogram Images of Breathing Sounds for COVID-19 and other Pulmonary Abnormalities

description: The dataset contains the spectrogram images of the real and generated human breathing sounds. The real breathing sounds are procured from Covid-19 patients as well as people not affected by Covid-19 (normal). The generated breathing sounds are procured from online medical repositories for educational purposes. The breathing sounds are segregated into 4 categories: Crackles-coarse, Crackles-fine, Wheezes, and Normal. The dataset comprises spectrogram images for - 420 coarse crackles (generated), - 375 fine crackles (generated), - 1143 normal (generated), - 322 wheezes (generated), - 1734 Covid-19 (real), - 2690 non Covid-19 (real)
DOI: 10.17632/sxyjnhvbm5.1
title: Validation data and mapping results for Tugay wetland detection at the Syr Darya based on MODIS and Landsat 7 NDVI time series

description: The data contains a zip-file with the validation point data (shp), mapping results (tiff, kml, txt), the area of interest (shp), and the Tugay wetlands NDVI reference profile (csv). The wetland maps are results from a linear regression based wetland detection analysis with Landsat 7 (LE7_L1T_8DAY_NDVI) and MODIS (MCD43A4_NDVI) time series data from Google Earth Engine (Gorelick et al., 2017) (Original Source: United States Geological Survey.) The trend and breakpoint detection maps are results of a time series analysis with the BFAST package (Verbesselt et al., 2010) which was done in the R statistical computing language (RStudio Team, 2020). The mean annual reference NDVI profile for Tugay wetlands was calculated for ten ground truth samples of riparian forest stands at the Syr Darya-Turkestan Nature Park. The validation data at the three 1500 km² large regions Kyzylorda, Zhanakorgan, and the Syr Darya-Turkestan Nature Park was derived from visual interpretation of very high spatial resolution Digital Globe images via Google Earth. The regions were chosen based on on a field trip in September 2017. This dataset is a contribution of the ForeCeA-project “Riparian forests in arid regions of Central Asia under pressure by use and limited water resources: definition of tipping points to ensure sustainable management” funded by the German Ministry of Research and Education (01LC1707C). For further information, we refer to the article mentioned in the references.
DOI: 10.17632/cm93t45psx
title: Raw data from data presented in Figures 1, 3, 4 and 5.

description: The data shown in this repository refer to those presented in Figure 1.
DOI: 10.17632/fgggdbrdnx.1
title: TNSPackage: A Fortran2003 library designed for tensor network state methods

description: Recently, the tensor network states (TNS) methods have proven to be very powerful tools to investigate the strongly correlated many-particle physics in one and two dimensions. The implementation of TNS methods depends heavily on the operations of tensors, including contraction, permutation, reshaping tensors, SVD and so on. Unfortunately, the most popular computer languages for scientific computation, such as Fortran and C/C++ do not have a standard library for such operations, and therefore make the coding of TNS very tedious. We develop a Fortran2003 package that includes all kinds of basic tensor operations designed for TNS. It is user-friendly and flexible for different forms of TNS, and therefore greatly simplifies the coding work for the TNS methods.
DOI: 10.17632/c872f83pdz.4
title: NEURON - Matlab codes 201706

description: Basic code used for online and offline analysis of brain signals in the manuscript: "Spindles promote memory formation during NREM through triple phase-locking of cortical, thalamic and hippocampal rhythms," Latchoumane et al. 1. offline Slow Wave detection and triggering based on arduino hardware 2. offline slow oscillation detection 3. offline spindle and ripple detection 4. offline sleep stating (before specialist correction)
DOI: 10.17632/cshccr8w3h.1
title: Data & Code from Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures

description: Data & Code associated with Lenio S, Kerr WT, Watson M, Baker S, Bush C, Rajic A, Strom L. Validation of a predictive calculator to distinguish between patients presenting with dissociative versus epileptic seizures. Epilepsy & Behavior. 2020. Note that minor modifications may be necessary prior to running the code.