cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/85fn49psnx.3
title: Supplementary files for the article "Atypical fibroxanthoma and pleomorphic dermal sarcoma: Local recurrence and metastasis in a nationwide population-based cohort of 1118 patients"

description: Supplementary files for the article "Atypical fibroxanthoma and pleomorphic dermal sarcoma: Local recurrence and metastasis in a nationwide population-based cohort of 1118 patients"

Authors: Mathias Ørholt, MD1; Kiya Abebe, BMSc1; Louise E. Rasmussen, BMSc1; Frederik L. Aaberg, BMSc1; Lærke J. Lindskov, BMSc1; Grethe Schmidt, MD1; Anne Lene Wagenblast, MD1; Michael M. Petersen, MD, DMSc2; Anand C. Loya, MD3; Søren Daugaard, MD3; Mikkel Herly, MD, Ph.d.1,4; David Hebbelstrup Jensen, MD, Ph.d.1; Peter Vester-Glowinski, MD, Ph.d.1
DOI: 10.17632/94jf97jzc8.1
title: Dataset for pest classification in Mango farms from Indonesia

description: The infestation of pests affecting the Mango cultivation in Indonesia has an economic impact in the region. Following the recent development in the field of machine learning, the application of deep-learning models for multi-class pest-classification requires large collection of image samples upon which the algorithms can be trained. Addressing such a requirement the paper presents a detailed outline of the dataset collected from the Mango farms in Indonesia. The data consists of images captured from the Mango farms affected by 15-categories of pests which are identifiable through structural and visual deformity exhibited in the Mango leaves. The collection of the data involved the use of a low-cost sensing equipment that are commonly used by the farmers for collecting images from the farm. The collected data is subjected to two processes, namely the data augmentation process and training of the classification model. The dataset collection consists of 510 images that includes 15-caterogies of pests that affect Mango leaves along with the original appearance of the Mango leaves (resulting in 16-classes) collected over a period of 6 months. For the purposes of training the deep-learning neural network, the images are subjected to data augmentation to expand the dataset and to emulate closely the large-scale data collection process carried out by farmers. The outcome of the data augmentation process results in a total of 62,047 image samples, which are used to train the network. The multi-class classification framework. The training framework presented in the paper builds on the VGG-16 feature extractor and replaces the last 3-year network with a fully connected neural network layers resulting in 16-output classes. The dataset includes the annotation of the image samples for both original images captured from the field and the augmented image samples. Both the original and augmented data has been classified as training, validation and testing. The overall dataset is divided into 3-parts, namely version 0, version 1 and version 2. The version 0 consists of the original data set, with 310 images to be used for training, 103 images to be used for the validation and finally 97 images for testing. The version 1 of the dataset of includes 46,500 image samples for training, following the application of the data augmentation process followed by the 103 original images used for validation and 97 images for testing. Finally, the version 2 of the dataset uses 47, 500 images for training and 15, 450 images for validation and 97 images for the testing. The three versions of the dataset include images available in JPEG format. The visual appearance of the pests captured in the dataset provides an ideal testbed for benchmarking the performance of various deep-learning networks trained to detect specific categories of pests. In addition, the dataset also provides an opportunity to evaluate the impact of data augmentation techniques on the original dataset.
DOI: 10.17632/cd2rtzm23r.1
title: EndoSLAM Dataset

description: EndoSLAM dataset includes standard and capsule endoscopy data from ex-vivo porcine gastrointestinal tract organs with time-synchronized, high-precision 6D ground truth pose, as well as a video sequence recorded by a clinically in-use colonoscope from a fully representative silicon colon phantom with CT scan ground truth. Besides, it includes synthetically generated data with pixel-wise depth and 6D pose ground truth which facilitates the simulation to real domain adaptation algorithms. Enjoy the data!
DOI: 10.17632/y5sm4smnfr.3
title: Sheep Facial Expression Primary Data-set

description: The sheep facial expression primary dataset has been cropped of the collection sheep imagesfrom ImageNet, NADIS, Pixabay, Flickr, and Gettyimages with high resolutions. It consists of 1500 plus sheep face images which has two subset normal and abnormal sheep faces. The abnormality of sheep can be identify by ears, eyes and mouth of the sheep. If the ears pinna visible than have no pain else for less visible and not visible have low and high pain respectively. Similarly for eyes and mouth will depends upon V and U shape. Here we uploaded a sample subset and complete dataset. This dataset has been used in our coming research article in Agriculture, MDPI. We have aim to collect more images which will be helpful to find pain in sheep through deep learning. As deep learning needs big data to complete a desired task from scratch, while currently we used transfer learning to find abnormality of sheep from there faces with deep learning.
DOI: 10.17632/psyjzs5x3p.1
title: Data for: The Differential Effects of Time and Usage on the Brand Premiums of Automobiles

description: Longitudinal data on twin-cars prices
DOI: 10.17632/c35zbmn7j8.1
title: Data for: Accelerated Cycle Life Testing and Capacity Degradation Modeling of LiCoO2-graphite Cells
DOI: 10.17632/m84mygrsr3.3
title: Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells

description: Supplemental methods, figure, and table for the paper titled 'Time-dependent reduction of somatic variants in lichen striatus as evidence for the clearance of mutated skin cells'
DOI: 10.17632/mgw6hrhxm4.1
title: Mendeley Supplemental Table 1

description: Characteristics of the 56 DFSP patients with recurrence in our cohort after applying exclusion criteria
DOI: 10.17632/4cbnfccrsr.1
title: Weight Loss After Weight-Loss Surgery: The Mediating Role of Dichotomous Thinking

description: This dataset pertains to a cross-sectional investigation encompassing 129 individuals who have undergone weight-loss surgery. It features pre- and post-surgical measurements of weight and body mass index (BMI), alongside assessments of dietary restraint and dichotomous thinking. Analysis of the data substantiates the assertion that dichotomous thinking concerning food and diet acts as a mediator in the association between dietary restraint and the maintenance of weight loss subsequent to surgery. This finding holds clinical significance due to the measurable and modifiable nature of dichotomous thinking through psychological intervention. For further information, please refer to Marshall, Reay, & Bowman's (2024) work titled "Weight Loss After Weight-Loss Surgery: The Mediating Role of Dichotomous Thinking," published in Obesity Surgery. Note that the dataset in question contains no personally identifiable information, and participants provided informed consent for the dissemination of their de-identified research data in support of open science initiatives.
DOI: 10.17632/6zpxnhjxzz.1
title: Blood Smear Images Dataset for AI-based Malaria Parasite Prediction

description: The Dataset contain two categories of data: 
1. Raw Dataset: This contain the blood smear images with both negative and positive cells
2. The Annotated folder: This contain blood smear images with the equivalent annotation
DOI: 10.17632/srpxhpd37p.2
title: Two-D CT images of the host rock and layers of a sinter mound from Los Geysers (northern TMVB)

description: Raw CT images (8-bit *.png images) of the host rock and layers of a sinter mound from Los Geysers (northern TMVB).  The images should be stacked to generate 3-D models.
DOI: 10.17632/5kjnphrbsz.1
title: Pool Boiling Pre-CHF and Post-CHF Image Dataset - Small

description: A simple pool boiling image dataset including pre-CHF and post-CHF images, designed for training binary image classifiers. Used in Assignments 2&4 of technical elective MEEG 44403/54403 Machine Learning for Mechanical Engineers at the University of Arkansas.
DOI: 10.17632/63fpbc9cm4.1
title: UCTH Breast Cancer Dataset

description: Research Hypothesis: 
This study hypothesizes that there are significant associations between the diagnostic characteristics of patients, including age, menopause status, tumor size, presence of invasive nodes, affected breast, metastasis status, breast quadrant, history of breast conditions, and their breast cancer diagnosis result. 

Data Collection and Description:
The dataset of 213 patient observations was obtained from the University of Calabar Teaching Hospital cancer registry over 24 months (January 2019–August 2021). The data includes eleven features: year of diagnosis, age, menopause status, tumor size in cm, number of invasive nodes, breast (left or right) affected, metastasis (yes or no), quadrant of the breast affected, history of breast disease, and diagnosis result (benign or malignant).

Notable Findings:
Upon preliminary examination, the data shows variations in diagnosis results across different patient features. A noticeable trend is the higher prevalence of malignant results among patients with larger tumor sizes and the presence of invasive nodes. Additionally, postmenopausal women seem to have a higher rate of malignant diagnoses.

Interpretation and Usage:
The data can be analyzed using statistical and machine learning techniques to determine the strength and significance of associations between patient characteristics and breast cancer diagnosis. This can contribute to predictive modeling for the early detection and diagnosis of breast cancer.

However, the interpretation must consider potential limitations, such as missing data or bias in data collection. Furthermore, the data reflects patients from a single hospital, limiting the generalizability of the findings to wider populations.

The data could be valuable for healthcare professionals, researchers, or policymakers interested in understanding breast cancer diagnosis factors and improving healthcare strategies for breast cancer. It could also be used in patient education about risk factors associated with breast cancer.
DOI: 10.17632/zs269d9fvb.1
title: Dietary methionine links nutrition and metabolism to the efficacy of cancer therapies

description: Nutrition exerts profound effects on health and dietary interventions are commonly used to treat diseases of metabolic etiology. Although cancer has a substantial metabolic component1, the principles that define whether nutrition may be used to influence tumour outcome are unclear2. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. In contrast, whether specific dietary interventions could influence the metabolic pathways that are targeted in standard cancer therapies is not known. We now show that dietary restriction of methionine (MR), an essential amino acid, and the reduction of which has anti-aging and anti-obesogenic properties, influences cancer outcome through controlled and reproducible changes to one-carbon metabolism. This pathway metabolizes methionine and further is the target of a host of cancer interventions involving chemotherapy and radiation. MR produced therapeutic responses in chemoresistant RAS-driven colorectal cancer patient derived xenografts (PDXs) and autochthonous KRASG12D+/-;TP53-/- -driven soft tissue sarcomas resistant to radiation. Metabolomics revealed the therapeutic mechanisms to occur through tumour cell autonomous effects on the flux through one-carbon metabolism that impacted redox and nucleotide metabolism, thus interacting with the antimetabolite or radiation intervention. Finally, in a controlled and tolerated feeding study in humans, MR resulted in similar effects on systemic metabolism as obtained in responsive mice. These findings provide evidence that a targeted dietary manipulation can affect specific tumour cell metabolism to mediate broad aspects of cancer outcome.
DOI: 10.17632/npg4jn2s2v.1
title: SentimentViz: Leveraging RoBERTa in Python for Advanced Sentiment Analysis and Visualization in the FMCG Sector

description: In a data-driven world where governments and businesses seek insights from vast amounts of unstructured text data, sentiment analysis plays a pivotal role in decision-making. Sentiment analysis helps analyze cus-tomer experience, ultimately helping manage customer engagement. To build on this need for deeper sentiment understanding and scalable solutions, SentimentViz is an accelerator that leverages Python and chooses the best methodology for text-mining problems. It enables real-time analysis with robust visualization capabilities. In this study, the Sentiment Viz accelerator is leveraged to estimate the sentiment of 9 different Patanjali prod-ucts using a strong data science framework and best-of-the-class ML techniques. This accelerator can predict sentiment for various products and services, categorizing them as positive, negative, or neutral. Once deployed, it enables real-time sentiment mapping for any product or service.
DOI: 10.17632/7hzr5539ws.1
title: ITB-mBatik Dataset

description: Institute Teknologi Bandung (ITB) modern Batik Dataset was created and designed as the first-ever high-quality symmetric pattern consisting of  1,216 images taken straight from design files for unsupervised image generation tasks purposes. At first, the dataset was created as training data for more efficient GAN and used as a working substitute due to the difficulty of finding a large amount of high-quality authentic Batik. 

We would like to thank Nurul Yafi for his massive contributions to this research. Without his work, this research would not have been possible. All the pattern images used in this dataset were taken directly from his work and shared under CC BY NC SA License. If you would like to use this work commercially, contact him directly at yafi04[at]gmail.com
DOI: 10.17632/t2yvtwnjx7.2
title: The tumor microenvironment architecture correlates with risk of recurrence in head and neck squamous cell carcinoma

description: We performed highly multiplexed microscopy using CODEX on 9 surgical specimens of patients with stage II-IV HPV negative squamous cell carcinoma explants of the oral cavity and oropharynx (HNSCC).  We selected and validated a panel of 30 antibodies that allows the discrimination of the main leukocyte subsets infiltrating the tumor as well as auxiliary markers for tumor, vascular, and stromal. Fluorochrome conjugated reported oligonucleotides were hybridized on 12-cycle experiments with the Akoya PhenoCycler connected to a BZ-X700 microscope. Images were automatically acquired on 121 contiguous 20X fields (5.6mm x 4.2mm, resolution 377.47 nm/pixel) with 7 z-slices (1.5 µm/z pitch) and 30% tile overlap, with 10ms, 250 ms, 350 ms, and 350 ms acquisition time for DAPI, AF488, Cy3, and cy5 filters, respectively. The resulting 40,656 images per specimen were processed with the Akoya’s PhenoCycler™ Instrument Manager (version 1.3) and CODEX Processor (version 1.7.2) for segmentation using the default parameters. Cell phenotyping was performed in R using unsupervised clustering, followed by manual curation of clusters based on marker expression, morphology, and tissue localization. Cellular neighborhoods were identified implementing in R the neighborhood analysis from the NOLAN lab (https://github.com/nolanlab/NeighborhoodCoordination). We identified 14 conserved, distinct cellular neighborhoods that constitute a collection of components characteristic of the HNSCC infiltrating tumor microenvironment. This study highlights the power of CODEX immune fluorescence in immuno-monitoring, reveals the extraordinary complexity of the tumor microenvironment of HNSCC patients, and supports the notion that TLS1 and long-lived plasma cells are important determinants of antitumor immunity and tumor progression. HNSCC_results.csv table contains coordinates of all segmented objected, expression profiles, cell types and cellular neighborhood annotations.
DOI: 10.17632/h22kxj6ydt.1
title: PyVulDet-NER

description: The data in this repository is associated with a manuscript paper, entitled "Python Source Code Vulnerability Detection with Named Entity Recognition". The paper has been submitted to the "DevSecOps: Advances for Secure Software Development" special issue in the "Computers & Security" journal. This research is part of an in-progress dissertation for George Washington University. In addition to the data shown in this repository, the following NER models were created with this data to identify 6 vulnerability types in Python source code:
https://huggingface.co/mmeberg/RoRo_PyVulDet_NER
https://huggingface.co/mmeberg/RoCo_PyVulDet_NER
https://huggingface.co/mmeberg/DiDi_PyVulDet_NER
https://huggingface.co/mmeberg/CoRo_PyVulDet_NER
https://huggingface.co/mmeberg/CoCo_PyVulDet_NER
DOI: 10.17632/rdsnzpzrkm.2
title: Datasets of the Systematic review and meta-analysis on incidence and seroprevalence of Rabies virus in humans, dogs and other animal species in Africa.

description: Accurate and reliable data on the burden of Rabies virus (RABV) can help to put in place better strategies to curb this burden in Africa among humans, dogs, and other animal species. To date, data synthesis on the epidemiology of Rabies virus (RABV) incidence and prevalence in the continent are lacking. Datasets included here consists of detailed information on the published studies on the prevalence based on of Rabies virus (RABV) among humans, dogs, and other animal species in Africa. The complete details of included studies and the reasons of those excluded is included in the dataset. We also have the database used for the meta-analysis and meta-regression.
DOI: 10.17632/x5mk2rg8yg.2
title: ATOM to NUCLEUS

description: In, the present article for support and favour of past articles E=F/s, and unavoidably others articles. I am discussing new logic and formulae , also I request to study each article discussed in the past. Without, unit power I have shown only numbers [Like : approx mass of one particle (Neutron) 1.679616 x 10-27 Kg in that place 1.679616 unit] the whole calculation to express present unit by using 10, or multiple of 10, We can make Different kind of units, either unit to unit like Kg, grm, Joule, Ev. Etc. each others. In the past the I have discuss about this a lot of times.
DOI: 10.17632/vv3pmvxwgn.2
title: Longitudinal data assessing self-concept clarity, dissociation, self-criticism, anxiety and psychological distress

description: Undergraduate students filled online questionnaires at base line (N=200) and 3 months later (N=157) assessing dissociative experiences, self-concept clarity, self-criticism, anxiety, COVID-19 anxiety, psychological distress, childhood trauma and demographic information. 
Dissociation variables (total score and subscales) and the BAI score were skewed at both Time 1 and Time 2, and therefore were transformed using squared root (total DES and BAI score at both Time 1 and Time 2) or log10 (ABS, DEP/DER and AMN at both Time 1 and Time 2) transformations. Univariate outliers were substituted by one unit of measurement above the next highest value.
DOI: 10.17632/74c83f3tf9.1
title: Development of Quality Criteria To Evaluate Nontherapeutic Studies of Incidence, Prevalence, or Risk Factors of Chronic Diseases: Pilot Study of New Checklists

description: We developed two checklists, one for studies of incidence or prevalence (MORE) and another for risk factors (MEVORECH) , based on our literature review and in collaboration with experts from other Evidence-based Practice Centers and the Centers for Disease Control and Prevention
DOI: 10.17632/7d7dfhrbzb.1
title: Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines

description: Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines Tamás Ruppert and János Abonyi Industry 4.0-based human-in-the-loop cyber-physical production systems are transforming the industrial workforce to accommodate the ever-increasing variability of production. Real-time operator support and performance monitoring require accurate information on the activities of operators. The problem with tracing hundreds of activity times is critical due to the enormous variability and complexity of products. To handle this problem a software-sensor-based activity-time and performance measurement system is proposed. To ensure a real-time connection between operator performance and varying product complexity, fixture sensors and an indoor positioning system (IPS) were designed and this multi sensor data merged with product-relevant information. The proposed model-based performance monitoring system tracks the recursively estimated parameters of the activity-time estimation model. As the estimation problem can be ill-conditioned and poor raw sensor data can result in unrealistic parameter estimates, constraints were introduced into the parameter-estimation algorithm to increase the robustness of the software sensor. The applicability of the proposed methodology is demonstrated on a well-documented benchmark problem of a wire harness manufacturing process. The fully reproducible and realistic simulation study confirms that the indoor positioning system-based integration of primary sensor signals and product-relevant information can be efficiently utilized in terms of the constrained recursive estimation of the operator activity. The Excel file has contained the data for the described use case. The Matlab codes that generate all the results (and figures) is also available in this repository.
DOI: 10.17632/n2jry7c537.1
title: Uzbekistan Railways

description: Set of data that was used in the article is secondary, that is, processed information, as well as some information was provided directly by the Uzbekistan Railways JSC
DOI: 10.17632/3xpbkmjfdz.1
title: Soil erodibility in Borgu-Agwara watershed

description: Field data showing erodibility indices and the coordinates from the Borgu-Agwara watershed, Niger State, Nigeria.
DOI: 10.17632/fdv9m38c2f.2
title: The Effect of Female Leadership on Contracting from Capitol Hill to Main Street

description: Code and data to replicate results in: "The Effect of Female Leadership on Contracting from Capitol Hill to Main Street" by Jonathan Brogaard, Nataliya Gerasimova, and Maximilian Rohrer, forthcoming in the Journal of Financial Economics. Please see the README file for details on files and code provided.
DOI: 10.17632/rbr7mjgpy5.3
title: Philippine Dengue Cases 

description: Dengue is a viral disease spread by Aedes aegypti mosquitoes. It is a problem in many tropical and subtropical parts of the world including Africa, Southeast Asia, and South America. In the Philippines, the viral disease is still endemic in all regions wherein annual cases have ranged from 200,000 to 400,000. 

In this dataset, the weekly cumulative confirmed cases of Dengue  in the Philippines from January 1, 2017 to October 8, 2022  were collected from the Philippine Department of Health website. The Excel file has three sheets: Sheet 1 contains the raw data that was extracted from the DOH website; Sheet 2 contains the raw, computed (Δ(X_n-X_(n-1))), and imputed data that were used in building the ARIMA-GARCH and HW models; and, Sheet 3 contains the forecasts from the models considered.

The data are useful as they as they can be used to train predictive models that can produce short-term forecasts of Dengue cases in the Philippines. These data can provide dynamic information to health officials and other concerned departments and agencies for surveillance, analysis, policy making, and decision making.  The data are reusable and can be used to further explore the dengue cases in the Philippines.