cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vbs8kbv9wt.1
title: Experimental data set from a GFRP experimental benchmark case for validating the simulation of fatigue-driven delamination in composite laminates

description: This data repository provides the raw and processed data to reproduce the experimental fatigue test results presented in the article entitled "Fatigue-driven delamination in composite laminates: Simulation with the inter-laminar damage model implemented in SAMCEF and validation against a GFRP experimental benchmark case" by the same authors. In this work,  experimental results from a demonstrator specimen made of a non-crimp fabric laminated Glass Fiber Reinforced Polymer (GFRP) used in the wind energy industry are provided. The selected benchmark case shows varying crack growth rates and crack front shape over the fatigue life, making it more representative of structures in service than coupon specimens. 

The test configuration, the methodology and the data processing are addressed in the related article. Data from Figure 7 is provided here. Five coupon specimens were tested to obtain the mode I inter-laminar fatigue crack growth rate curves. The Matlab scripts for data treatment are also included. In addition, data from Appendix B is provided. Three demonstrator specimens were tested to provide crack front geometries during the fatigue testing. In addition, a complete set of elastic, fracture and fatigue properties for the material are provided in the article. This data set, in conjunction with the associated article, constitutes a comprehensive benchmark test against which existing or future delamination growth models can be evaluated.
DOI: 10.17632/s3gk8cbj39.1
title: Political Ideology and International Capital Allocation

description: Replication code, data, and description for "Political Ideology and International Capital Allocation," accepted at the Journal of Financial Economics.
DOI: 10.17632/xw5xc7xrmp.3
title: HVDROPDB Datasets for Classification and Segmentation for Research in Retinopathy of Prematurity, Ranjana Agrawal

description: HVDROPDB_RetCam_Neo_Segmentation and HVDROPDB_RetCam_Neo_Classification are the first datasets to be published for the retinal structure segmentation to identify the Retinopathy of Prematurity (ROP). They are prepared by screening the preterm infants visiting PBMA's H.V. Desai Eye Hospital, Pune with two diverse imaging systems RetCam and Neo. The Segmentation dataset contains sub-datasets for the segmentation of optic disc, blood vessels, and demarcation line/ridge from the fundus images of preterm infants, annotated by a group of ROP experts. Each sub-dataset contains retinal fundus images of premature infants with the ground truths prepared manually to assist researchers in developing an explainable automated ROP screening system. The Classification sub-datasets contain ROP and Normal images.
DOI: 10.17632/rnmxfv6pyn.2
title: Future Weather Files (under Climate Change Scenarios) to Support Building Energy Simulation

description: The future weather files are synthesized based on the methodology presented in the research project on the impact of climate change on building energy performance. These weather files (in EPW format) are intended to be used for building energy simulation and performance evaluation. The team provides here future weather files for 30 years (from 2020 to 2049) for four Representative Concentration Pathways (RCP2.6, 4.5, 6.0, and 8.5) used in the Fifth IPCC (Intergovernmental Panel on Climate Change) Assessment. Due to limited resources, Canada - Montreal (WMO 716270) is the first set of data available. Weather files for other locations and scenarios will gradually be added. If you have any pressing needs, you are welcome to contact the team to explore the possibility. The future weather files are synthesized based on a workflow developed by the team. A full explanation is presented in our article. The following is a brief description of the workflow: The effect of climate change is based on the General Circulation Models (GCM). First, a bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adopt GCMs to a specific location. A hybrid classification-regression model is used to downscale the bias-corrected GCM data to synthesize future weather data at an hourly resolution for building energy simulation. The proposed workflow enables users to use a set of observed weather data by finding similar patterns rather than artificially generating data. However, in cases where the future GCM data show temperatures outside the range of the observed data, a trained regression model is applied to synthesize hourly weather data. If readers are interested in the topic, please browse through related articles of the team: Generating future weather files under climate change scenarios to support building energy simulation — a machine learning approach https://doi.org/10.1016/j.enbuild.2020.110543 A systematic approach in constructing typical meteorological year weather files using machine learning https://doi.org/10.1016/j.enbuild.2020.110375 Cooling and heating energy performance of a building with a variety of roof designs; the effects of future weather data in a cold climate https://doi.org/10.1016/j.jobe.2018.02.001 Energy performance of cool roofs under the impact of actual weather data https://doi.org/10.1016/j.enbuild.2017.04.006 Version History: Version 1: WMO 716270, RCP 4.5 only Version 2: WMO 716270, RCP 2.6, 6.0, 8.5 added Disclaimer: The weather files provided here are synthesized based on the novel workflow developed by the research team with limitations and based on assumptions. A full description of the workflow is presented in our articles. We do not make any warranties about the completeness, reliability, and accuracy of the data. Any use of the data is strictly at your own risk, and we will not be liable for any losses and damages in connection with the use.
DOI: 10.17632/hnd59x6gmg.1
title: The climatic and genetic heritage of Italian goat breeds with genomic SNP data

description: Genotype data in PLINK binary format associated with the following paper: Cortellari et al., 2021. 'The climatic and genetic heritage of Italian goat breeds with genomic SNP data' Scientific Reports. DOI: 10.1038/s41598-021-89900-2
DOI: 10.17632/sfv89789x4.1
title: Comprehensive earthquake-induced landslide inventory dataset of the 2010 Chile megathrust earthquake

description: The dataset was collected from bibliographic compilation, mapping by interpretation of Landsat satellite images, and field inspections; for the bibliographic compilation reviewed 107 technical reports of the National Geological and Mining Survey of Chile (SERNAGEOMIN) related to the Maule earthquake, from which the relevant information of landslides and lateral spreads was extracted. Also, the georeferenced reports of road network interruption problems caused by the Maule earthquake, undertaken by the Ministry of Public Works, were reviewed. Finally, it incorporated an inventory of lateral spreads provided by Verdugo (2012) and the inventory of landslides in the coastal fringe of the Biobio administrative region provided by Mardones and Rojas (2012). Additionally, coseismic landslides were mapped by using Google Earth to interpret Landsat satellite images (Landsat 5-7-8; provider, NASA; resolution, 30 m) before and after the earthquake. These strips were visually inspected at an eye height of ~ 1–2 km, decreasing the height when an alteration was detected in the vegetation or when bare spots or typical mass movement morphologies were present (Soeters and Van Western, 1996). ]. It was visually inspected, and the earliest available images after the earthquake were mapped at 1:2000 and 1:10,000. Once a landslide was identified, the location was compared with the latest preseismic image without cloud or snow cover and the landslide was mapped as a polygon. Field inspections were undertaken in the coastal regions, where the higher densities of landslides are located. The minimum size considered for the mapping was 30 m2, although field inspections showed that an indefinite number of small mass movements were not recognised on the satellite images.
DOI: 10.17632/xgndv6h3nn.2
title: OPTIMIZATIONS OF AIRCRAFT PARKING CAPACITY (APRON) IN MINANGKABAU INTERNATIONAL AIRPORT PADANG

description: Optimizations of aircraft parking area (apron) Minangkabau International Airport can be done by reduction of parking stand from 7 to 6 with aircraft parked type is medium body aircraft and ask the operators to consistent with the assigned slot time or propose revision of slot time in spare time (non – peak hours)
DOI: 10.17632/xrsbyjs24t.1
title: Predictive Clinical Dataset for Dengue Fever Using Vital Signs and Blood Parameters

description: Dengue fever remains a serious public health issue in Bangladesh, particularly in regions like Kalai, Joypurhat, where favorable conditions lead to regular outbreaks. The lack of early diagnostic tools and the limited availability of healthcare infrastructure amplify the severity of these outbreaks. In light of these challenges, a comprehensive clinical and hematological dataset has been developed to support the prediction of dengue fever, aiming to assist in early diagnosis and improve patient outcomes.

Dataset Overview: This dataset includes vital signs and hematological parameters from patients diagnosed with dengue fever. The data aims to support the creation of predictive models that use machine learning algorithms to estimate the likelihood and severity of dengue based on clinical parameters. The dataset is ideal for researchers, healthcare professionals, and public health officials who are working to improve diagnosis, treatment, and epidemiological management of dengue fever.

Clinical Parameters Included:

Age: Patient’s age in years.
Sex: Gender of the patient (Male/Female).
Hemoglobin (g/dL): Hemoglobin levels in the blood.
WBC Count (x10^3/µL): Total white blood cell count.
Differential Count (%): Distribution of different types of white blood cells.
RBC Panel: Detailed red blood cell measurements, including counts and morphology.
Platelet Count (x10^3/µL): Total number of platelets in the blood.
PDW (%): Platelet distribution width, which indicates variability in platelet size.
Data Collection: The dataset was compiled from patients at the Upazila Health Complex in Kalai, Joypurhat. All data collection procedures followed ethical guidelines, with patient information anonymized to protect confidentiality.

Structure of the Dataset:

Format: CSV
Rows: 1,003 (individual patient records)
Columns: 9 (clinical parameters)
DOI: 10.17632/vzbrzvyrn4.2
title: The TDHF code Sky3D version 1.2

description: The Sky3D code has been widely used to describe nuclear ground states, collective vibrational excitations, and heavy-ion collisions. The approach is based on Skyrme forces or related energy density functionals. The static and dynamic equations are solved on a three-dimensional grid, and pairing is been implemented in the BCS approximation. This updated version of the code aims to facilitate the calculation of nuclear strength functions in the regime of linear response theory, while retaining all existing functionality and use cases. The strength functions are benchmarked against available RPA codes, and the user has the freedom of choice when selecting the nature of external excitation (from monopole to hexadecapole and more). Some utility programs are also provided that calculate the strength function from the time-dependent output of the dynamic calculations of the Sky3D code.
DOI: 10.17632/vpb6m7gmhn.2
title: SUPPLEMENTARY MATERIALS: Deucravacitinib versus placebo and apremilast in moderate to severe plaque psoriasis: efficacy and safety results from the 52-week, randomized, double-blinded, placebo-controlled phase 3 POETYK PSO-1 trial

description: Supplementary materials for the publication of Armstrong A. Deucravacitinib versus placebo and apremilast in moderate to severe plaque psoriasis: efficacy and safety results from the 52-week, randomized, double-blinded, placebo-controlled phase 3 POETYK PSO-1 trial. J Am Acad Dermatol 2022.
DOI: 10.17632/gsxphk87mc.3
title: EEG and psychological assessment datasets: Neurofeeedback for the treatment of PTSD

description: Psychological assessments were conducted through clinical interviews, to collect psychometric data for twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The participants were assigned to a control group (n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received a Brain-Computer Interface (BCI) based training as a treatment intervention over a period of two weeks between the pre- and post- clinical interviews. The training involved presenting feedback visually via a game, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.
DOI: 10.17632/k8c4g2mphp.1
title: Carambola Disease Recognition Dataset

description: 1. The scientific name of camranga is Averrhoa carambola Linn; the English name is Chinese gooseberry, or Carambola. It is a fruit tree belonging to the genus Averrhoa in the Oxalidaceae family. It is native to tropical Southeast Asia. 2. Camranga is a perennial green fruit of a small to medium-sized tree. The tree grows 15–25 feet tall with densely branched, spreading limbs and compound leaves that are 1-3 inches long. The fruit has a smooth, dark green skin. It is 3-6 inches in diameter and is angular in shape when mature. The fruit is green when unripe and turns yellow when ripe. Carambola can have a sour or tangy taste. Some trees bear fruit multiple times a year or consistently throughout the year. It is a good source of vitamins A and C. The fruit is typically available from September to January. 3. The whole fruit can be eaten, including the thin skin. Its organic acids help prevent cancer in the digestive system. Tannins in the juice of the leaves and unripe fruit assist in preventing the accumulation of blood clots. It is used as a primary detoxifier. The dried fruit is used for fever. Due to its cooling and tangy nature, it works as an effective remedy for heat-related issues, coughs, and as an anti-flatulent. Consuming camranga helps improve appetite and digestion. 4. Protecting camranga from pests is crucial for a successful fruit harvest. There are various pests that can attack camranga plants, and farmers need to take measures to safeguard their fruit crops. This may involve using pesticides, implementing integrated pest management techniques, or adopting organic farming methods to minimize the use of chemicals and ensure healthy crop yields. 5. Diseases in fruit crops are a widespread problem in agricultural production, and they have an adverse impact on the quality and productivity of the crops. Due to non-biological environmental factors, camranga diseases adversely affect the yield and quality of camranga, causing significant economic losses to farmers. The traditional identification of crop diseases is often time-consuming, labor-intensive, ineffective, and subjective. 6. In recent times, computer vision has shown great promise in conducting the classification and identification tasks of this kind. 7. A large dataset of nine different carambola classes—Healthy Fruit, Healthy Leaf, Stem Borer Disease, Anthracnose Disease, Leaf Insect Pest Disease, Early Blight Disease, Hooded Hopper Insect Disease, Bed Bugs Disease, and Fruit Borer Disease—is shown in order to create machine vision-based algorithms. 8. In reality, 559 images of carambola were initially collected from the field. Subsequently, to increase the quantity of data points, various image processing techniques were applied, such as shifting, flipping, zooming, shearing, brightness enhancement, and rotation, resulting in a total of 3913 augmented images derived from the original images.
DOI: 10.17632/wwcx52zk7t.1
title: Efficient energy basin finding algorithm for self-learning kinetic Monte Carlo method

description: Data for the manuscript "Efficient energy basin finding algorithm for self-learning kinetic Monte Carlo method".
DOI: 10.17632/z5khb77xyd.1
title: Forensic Genetic Genealogy Project v. JULY2024

description: This research seeks to collect all publicly available information concerning cases that were successfully cleared through forensic investigative genetic genealogy (FIGG), going back as far as possible in time and ending on July 31, 2023. 

Cases were selected for inclusion in the dataset through internet searches, discussion boards, as well as press releases and blog posts from private companies and non-profit organizations known to perform FIGG, publications devoted to advances in forensic science, and published court records. 

Cases were chosen for inclusion in the sample only if a public authority had confirmed that FIGG was used to clear the investigation, and the investigation met the following definition of FIGG:

1. Investigative Body: The investigation was led by a public, and not a private, authority, such as a law enforcement agency or a coroner/medical examiner’s office. 

2. Investigative Techniques: The investigation must use the techniques of genomics, computer database technologies, and traditional genealogy. Cases were included when family lineages were drawn out to at least three or more generations. The sample includes investigations that use Y-chromosome STR profiles and/or mitochondrial DNA profiles to trace the paternal and maternal lineages of persons of interest. Cases solved through familial searching of law enforcement databases were excluded from the sample, as this technique raises separate legal and ethical issues. 

3. Investigative Purpose: The investigation must be for a forensic purpose, meaning that the evidence thus produced is relevant to an ongoing legal dispute. Most cases considered here are for the identification of perpetrators of criminal offences or the identification of decedents but can also include the identification of living does. Not included in this data are cases that are of purely historical interest, or the identification of unknown soldiers by military authorities. The term ‘forensic genetic genealogy’ has been chosen for this project in preference to the broader, and more generic term ‘investigative genetic genealogy’, which has also been used, to highlight the fact that these investigations are directed towards an ongoing legal dispute before the public legal system. 

4. Causation: This database only includes cases that have been successfully cleared using FIGG. The resolution of any investigation – particularly very large and lengthy investigations as were many of the cases described in this data – has many causes. To be included here, the FIGG investigation must have played a significant causal role in the resolution of the case. 

More complete information about how the data was coded, as well as an annotated bibliography and a short digest of each case, can be found in the Code Book, included in previous versions of this project. You may read a short digest of each case cleared, including references for all data collected, in the Case Digests below.
DOI: 10.17632/8vknc5kzmf.1
title: Radiomics based likelihood functions for cancer diagnosis

description: Feature selection of lung nodule in CT data using several algorithms. Features are reduced and reliability is checked. Based on selected features , likelihood of lung cancer is formulated. Two functions are devised .
DOI: 10.17632/tdszj84x29.1
title: Dataset of optimal reinforced concrete structural elements: Beam and column sections

description: This repository houses a meticulously curated dataset of optimal reinforced concrete structural elements, specifically focusing on beam and column sections. The data is generated leveraging the Section-Based DataBase Schema (SBDBS) framework, a robust mechanism hosted on the Mendeley Data repository, dedicated to the creation of optimal reinforced concrete sections. The dataset extends a profound insight into the design parameters and load capacities of the optimal beam and column sections, encapsulating essential metrics like dimensions, number of rebars, moment capacities, and cost per meter. The tabulated design parameters and load capacities of optimal beam sections, showcasing a comprehensive blend of data ranging from dimensions (B, H) to load capacities (Mn+, Mu+, Mn-, Mu-) and cost. In a similar vein, the design parameters and load capacities of optimal column sections, provided a deep dive into the critical attributes of columns, hence offering a rich resource for both academic and practical endeavors in the realm of structural engineering. Moreover, this dataset serves as an indispensable asset for researchers, engineers, and decision-makers aiming to delve into cost-effective, robust, and optimized design of reinforced concrete structures. By offering a tangible glimpse into the optimal configurations, this dataset significantly contributes to advancing the narrative of structural optimization, fostering informed decision-making, and propelling forward the frontiers of structural design optimization.
DOI: 10.17632/bj8pv6x5jw.1
title: Two heads are less bubbly than one: team decision-making in an experimental asset market

description: Instructions, software and results reported in Cheung, S. L., and S. Palan (2012): “Two Heads are Less Bubbly than One,” Experimental Economics, 15, 373–397, http://dx.doi.org/10.1007/s10683-011-9304-6.
DOI: 10.17632/jkzsh6pcv4.1
title: Fundus Image Myopia Development (FIMD) dataset

description: Fundus Image Myopia Development (FIMD) dataset contains 70 retinal image pairs, in which, there is obvious myopia development between each pair of images. In addition, each pair of retinal images has a large overlap area, and there is no other retinopathy. In order to perform a reliable quantitative evaluation of registration results, we follow the annotation method of Fundus Image Registration (FIRE) dataset [1] to label control points between the pair of retinal images with the help of experienced ophthalmologists. Each image pair is labeled with 12 pairs of widely and uniformly distributed control points, which are mainly located at the bifurcation and crossing of blood vessels. FIMD also records the corresponding vision screening data of each image, including age, gender and uncorrected visual acuity. It should be noted that the time interval between two images is relatively long (one to five years), and the fundus cameras are different, which leads to different resolution, brightness, contrast and field of vision between two images. 
Moreover, the structure of our FIMD dataset is shown in "FIMD-dataset-structure.txt". You can conduct registration studies on image pairs in the dataset, and then evaluate the registration effect through annotated control points. In addition, we also record the age, gender, and uncorrected visual acuity for each image, so related study on the development of myopia may also be performed on this dataset. It depends on your own thinking. 

Reference: [1] Hernandez-Matas C, Zabulis X, Triantafyllou A, et al. FIRE: fundus image registration dataset[J]. Modeling and Artificial Intelligence in Ophthalmology, 2017, 1(4): 16-28.
DOI: 10.17632/fsvsjv85yw.1
title: Hawaii Receiver Function Dataset, Results, & Code

description: Open data for "Constraining the Lithospheric Discontinuity Structure beneath Hawaiʻi using Teleseismic Receiver Functions"  (Herr & Delph, 2023).
DOI: 10.17632/fk98kt2r85.1
title: Data for: INTEGRATION OF GREENHOUSE AGRICULTURE TO THE ENERGY INFRASTRUCTURE AS AN ALIMENTARY SOLUTION

description: Assumptions, calculations and results for the article submission titled "INTEGRATION OF GREENHOUSE AGRICULTURE TO THE ENERGY INFRASTRUCTURE AS AN ALIMENTARY SOLUTION"