cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xs6mvhx6rh.1
title: ASLYset

description: This dataset consist in 5,200 images of size 416×416 obtained from four different persons, performing 24 ASL alphabet signs (whole alphabet excluding “J” and “Z”) and two additional signs called “SP” and “FN”, each volunteer generated 50 images for each of the 26 signs. All of the images are labelled using YOLO format for training YOLO networks for object detection. Besides the here presented data was generated for training YOLO networks, it can be can be useful for training other computer vision algorithms (e.g. Faster R-CNN) by properly labelling images. Images were extracted using the right hand at a varying distance (hand to camera) from 20 to 40 cm approximately, and moving the hand around the scene. Every image was labelled using the freely available software labelImg [1].
DOI: 10.17632/bm5d7xk6gw.1
title: TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions

description: Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel Python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains.
DOI: 10.17632/xwyg8pzvdd.1
title: Data for: Emplacement dynamics and hydrothermal alteration of the Atengo ignimbrite, southern Sierra Madre Occidental, northwestern Mexico

description: the files are raw, which are generated during hysteresis and FORC analysis
DOI: 10.17632/4whpm32y47.7
title: Entity Resolution Benchmark Datasets

description: This collection of contains real and synthetic data benchmark datasets along with their complete golden standard (i.e., groundthruth of duplicate entities) for assessing the performance of various end-to-end ER workflows using JedAI.
DOI: 10.17632/sxybkgc5xc.1
title: DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

description: In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN.
DOI: 10.17632/wzr2yv7r53.1
title: 2D geometric shapes dataset

description: This dataset is composed of 2D 9 geometric shapes, each shape is drawn randomly on a 200x200 RGB image. During the generation of this dataset, the perimeter and the position of each shape are selected randomly and independently for each image, the rotation angle of each shape is selected randomly for each image within an interval between -180° and 180°, as well the background colour of each image and the filling colour of each shape is selected randomly and independently. The published dataset is composed of 9 data classes, each class represent a type of geometric shape (Triangle, Square, Pentagon, Hexagon, Heptagon, Octagon, Nonagon, Circle and Star). Each class is composed of 10k generated image. This paper includes also a GitHub URL to the generator source code used for the generation which can be reused to generate any desired size of data. The proposed dataset aims to provide a perfectly clean dataset, for classification as well clustering purposes, the fact that this dataset is generated synthetically provides the ability to use it to study the behaviour of machine learning models independently of the nature of the dataset or the possible noise or data leak that can be found in any other datasets. Moreover, the choice of a 2D geometrical shape dataset provides the ability to understand as well to have good knowledge of the number of patterns stored inside each data class.
DOI: 10.17632/9rzgv6xd57.1
title: Air Pollutants Dataset of Pune City, India

description: This data set includes hourly air pollutant data from   air quality monitoring station of MIT-WPU, Kothrud, Pune, India. The data is collected from (SAFAR-India), System of Air Quality and Weather Forecasting and Research, Ministry of Earth Science, Govt. of India, Indian Institute of Tropical Meteorology, Pune.  The data collected is for the period from 5/06/2019 to 29/06/2022 with 26891 records. The attributes are air pollutants: PM10, PM2.5, PM1, O3, CO, NO, NO2, NOx, CO2.  This data could be very well used for air pollutant analysis, evaluations, calibration of sensors and study of predictive models for air pollutants.
DOI: 10.17632/gky8n73grd.1
title: PANEL DATA

description: Research data of the paper is included by name of 'panel data'. All related variables of 32 Asian countries are given.
DOI: 10.17632/bhs5s87gfp.1
title: covid-airpollution

description: city wise covid incidence and deaths for India and air pollution such as pm2.5, NO, SO etc... of 2020 over world
DOI: 10.17632/x4dwwfwtw3.3
title: TEM virus dataset

description: The dataset contains in total 1245 images of 22 virus classes captured with two different electron microscopes: an LEO (Zeiss, Oberkochen, Germany) with a Morada (Olympus) camera and a Tecnai 10 (FEI, Hillsboro, OR, USA) with a MegaView III (Olympus, Münster, Germany) camera. Before imaging, all samples were treated with 10% phosphate-buffered saline, placed on carbon-coated TEM grids, and stained with 2% phosphotungstic acid following standard procedures. The virus image dataset is challenging due to many reasons: limited annotation (a center point/line and not a full segmentation mask), a relatively small number of images per class, diffuse virus boundaries, imperfect focus, noise, different magnifications, and a large variation of the virus, background, and debris appearance. The virus classes in the dataset are strongly unbalanced both regarding the number of images (from 9 to 129) and in the number of virus particles (from 38 to 1934). The sizes of all RAW images are either 1376 x 1032 or 2048 x 2048 pixels (depending on with which electron microscope they were captured) but the pixel sizes vary from 0.26 to 5.57 nm, i.e., they were acquired at different magnifications. Each virus particle is annotated only with its approximate center, i.e., a single point for isolated spherical particles or a centerline in case of clustered viruses (beyond visual recognition of individual particles) or elongated virus. The annotations are in the form of coordinate points stored in a separate text file for each image. We also include image patches of 256 x 256 pixels (256 x 256 nm). The patches were cropped from rescaled images around the annotation points: the manually selected center points for spherical virus particles and all center-line vertices for elongated virus particles, except for the elongated particles that were annotated with a center-line composed of only 2 points (this would usually indicate an oval-shaped virus particle as in e.g. Orf) – in this case, we selected a new point between them for the center of the patch. This resulted in more image patches than particles in virus classes with elongated particles, particularly in Marburg, Ebola, Influenza, Lassa, and Nipah. As many annotation points were placed relatively close to each other (due to natural clustering of the virus particles and/or complex shapes of the elongated particles), the patches cropped from the same image sometimes overlap with each other to some degree. However, this did not lead to a data leakage between the training, validation, and test sets because they were established at the image level and special care was taken to remove images from the dataset that captured the same virus particles (i.e., overlapping images). We augmented the training set image patches by flipping and multiple 90 degrees rotations so that each class contains 736 input samples. Those classes that originally contained more than 736 patches were randomly reduced to this number.
DOI: 10.17632/jd2c2w8th2.1
title: Data for: The infant's face diet: Data on 3-month-old infant-perspective experience with faces video-recorded in their typical, daily environment

description: This dataset represents face experience coded frame-by-frame from nearly 170 hours of infant-perspective head-mounted-camera video, recorded during their daily life by 40 3-month-old infants. It includes information about the identity of the face (e.g., caregiver, relative), length of time the face was in the field of view, location in which the face occurred, and descriptions of the situation in which the infant experienced the face. Demographic information (e.g., age, gender) about the infants who recorded the videos is also provided. For elaboration on data collection methodology, interpretation, analysis, and discussion of early face experience captured by this dataset, please see our paper These are the people in your neighbourhood: Consistency and persistence in infants’ exposure to caregivers’, relatives’, and strangers’ faces across contexts [1].
DOI: 10.17632/jtwtrbhfcb.3
title: Equilibrium Model Solver 

description: The research summarises an advanced, multifarious thermodynamic equilibrium model of a downdraft biomass gasifier. The model solver incorporates the operating conditions needed to solve the system of equations along with the multi-equation system. The code is able to predict the syngas composition at different operating parameters, the lower heating value and cold gas efficiency efficiency of the syngas and the tar and char yields. The code can be manipulated accordingly to predict results of different biomass feedstock by changing the ultimate analysis and heating properties of the feedstock.
DOI: 10.17632/w6wvrt8dnp.1
title: Data for: From Field to Fish: Tracking Changes in Diet on Entry to Two Medieval Friaries in Northern England
DOI: 10.17632/wvvsg5gkzr.2
title: Labor force participation and total factor productivity

description: This dataset contains a country panel that has IMF capital stocks (separated into infrastructure and non-infrastructure) and data on GDP, labor force, education, and demographics. From these, it is possible to derived total factor productivity levels for country-year and perform further analysis. The key for the research is to test if TFP differences between economies can be accounted for by male-female labor force participation gaps.  

As an alternative comparison, a heuristic TFP series created using Penn World is appended. This can be used as robustness checks.
DOI: 10.17632/8jtfk9szbg.2
title: Multifaceted Rose Leaf Disease Dataset for AI-Driven Plant Pathology

description: 1. Roses are highly valued in the horticultural industry due to their aesthetic appeal and monetary worth. However, numerous diseases that can seriously affect plant health and productivity often pose a challenge to their cultivation. It is essential to accurately and promptly identify leaf diseases in order to treat these issues. A carefully selected collection, the Rose Leaf Disease Dataset is intended to help horticulturists, researchers, and machine learning professionals identify and categorize illnesses that impact rose leaves. This collection of high-resolution photos of rose leaves displays a variety of situations, including diseases and healthy ones. It covers several prevalent rose leaf illnesses, such as Downey mildew and black spots. Extensive annotations are appended to every image, offering discernible information regarding the kind and intensity of the ailment, thus enabling accurate and dependable model training and validation. Furthermore, the information is enhanced with metadata that documents the geographical locations and ambient conditions of the image captured. This contextual data can improve the precision of predictive models and is essential for comprehending the variables influencing the occurrence of disease.
2. These days, deep learning and computer vision techniques hold a lot of promise for handling these kinds of classification and detection tasks.
3. A thorough dataset for Rose Leaf Disease is presented in order to develop deep learning methods. This dataset's classifications were created in cooperation with a subject matter expert from an agricultural institute.
4. A total of 1456 images of downy mildew (422), black spot(488), and fresh leaves (546) were gathered from the Savar demonstration location in Dhaka, Bangladesh. Then, in order to enhance the number of data points, 30,000 augmented images were created from these original images by applying techniques such as flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming.
DOI: 10.17632/dvnntk22h6.1
title: Cars Dataset (Positive and Negative)

description: The dataset is organized into 2 folders (positive and negative) which contains total 2,800 images. The data is split into 1,230 positive images (car images) and 1,570 negative images (background images). The dataset is arranged in such a way that HAAR Cascade Classifier training can be done with it. This structure is described in OpenCV docs (https://docs.opencv.org/3.4/dc/d88/tutorial_traincascade.html). Positive Directory structure: 'img' folder witch contains all positive images and 'info.dat' file which contains all positive image descriptions. The first element of the line is the filename, followed by the number of object annotations, followed by numbers describing the coordinates of the objects bounding rectangles (x, y, width, height). /img img1.jpg img2.jpg . . . . info.dat Inside the file info.dat: img/img1.jpg 1 140 100 45 45 img/img2.jpg 2 100 200 50 50 50 30 25 25 Negative Directory structure: 'img' folder witch contains all negative images and 'bg.txt' file witch contains all negative image directory list. /img -img1.jpg -img2.jpg . . . . bg.txt Inside the file bg.txt: img/img1.jpg img/img2.jpg . . . .
DOI: 10.17632/ppb8bhpmb4.3
title: Effect of psychotherapy on Intolerance of Uncertainty: A meta-analysis and review (META-ANALYSIS DATA)

description: Effect sizes, hedges g, gathered from treatment outcome articles on psychological treatments. Published between 1994 and 2022 march. Indicate an overall effect of treatment on Intolerance of uncertainty.
The following inclusion criteria was used for selection: (a) Randomized controlled trial on psychotherapeutic treatments for adult patients, reporting outcome measures on Intolerance of Uncertainty either as a primary or secondary measure. (b) Treatments are described in such detail that they are reproducible. (c) Treatment is delivered without augmentations such as psychopharmacological medication or other major therapeutic interventions, unless augmentations are equal across groups within the study. (d) Published in peer-reviewed journal. (e) Group and single patient delivery modes as well as online/digital delivery which are not dependent on sophisticated aps/games/programs beyond video/text communication. Meaning video conference-delivered therapy and online self-study programs that might have been delivered in analog form are included, but treatments based on differentially responding software or other automated functionality were excluded.
Quality assessment and data extraction
The quality of the eligible studies was assessed independently by the first and second author using the 13-item Joanna Briggs Institute Checklist for Randomized controlled trials (Joanna Briggs Institute, 2017).  Each item was classified as either Yes (critieria fulfilled), No (criteria not fulfilled) or Partial (criteria partially fulfilled or unclear description). Any disagreements were discussed and if not resolved successfully decided by the third and fourth author.
Coding of the study elements and outcomes was performed by the main author. The elements coded were participant characteristics (number total, males/females, mean age, clinical or non-clinical, primary diagnosis), intervention characteristics (length/number of sessions, face-to-face/online/phone/group/single patient) as well as type of control condition (wait-list, delayed treatment, non-specific therapy, treatment as usual). When there were multiple active groups with independent individual participants, data was extracted for all of them. In the case of Mathur et al (2021), Ladouceur et al., (2000) & Tomei et al., (2018), methodology described in Thalheimer & Cook was used to calculate an effect size.
DOI: 10.17632/9wk5drynd6.1
title: M-Commerce Dataset (1996-2020)

description: The M-Commerce research & publication dataset, which was indexed by Scopus from 1996 to 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID
DOI: 10.17632/vhjnxz7jb4.2
title: DC in TBI case-control

description: The impact of DC in children with moderate-to-severe TBI remains unclear.This retrospective matched case-control analysis was conducted on 53 children with moderate-to-severe TBI who underwent DC. Each child who underwent DC was matched with one child who did not undergo DC according to age, Glasgow coma scale (GCS), pupil response, and cranial CT findings on admission, and compare their mortality, Pediatric Cerebral Performance Category (PCPC) at discharge.
DOI: 10.17632/2xym4pdkgw.2
title: Climate change and plant rusts: A bibliometric analysis

description: Manuscript database "Climate change and plant rusts: A bibliometric analysis".
DOI: 10.17632/tp5bpph5y5.1
title: Platelet Factor 4 Antibodies and Severe AKI

description: Raw data and data dictionary for the paper, "Platelet Factor 4 Antibodies and Severe AKI" published in Kidney360 (DOI: 10.34067/KID.0000000000000287). [NOTE: ALL DATA HAS BEEN DE-IDENTIFIED, ANONYMIZED, AND DATE-SHIFTED]

Abstract
Background: 
Heparin-induced thrombocytopenia, which results from production of antibodies that bind to heparin-platelet factor 4 (PF4) complexes, is a hypercoagulable state associated with considerable morbidity and mortality due to thrombotic complications. We investigated whether PF4 antibodies are associated with an increased risk of acute kidney injury (AKI).

Methods: 
We conducted a cohort study of hospitalized adults who underwent testing for PF4 antibodies at two large medical centers in Boston between 2015 and 2021. The primary exposure was PF4 test positivity. The primary outcome was severe AKI, defined by KDIGO stage 3 as a ≥3-fold increase in serum creatinine or receipt of kidney replacement therapy within 7 days following the PF4 test. We used multivariable logistic regression to adjust for potential confounders.

Results: 
A total of 4224 patients were included in our analysis, 469 (11.1%) of whom had a positive PF4 test. Severe AKI occurred in 50 of 469 patients (10.7%) with a positive PF4 test and in 235 of 3755 (6.3%) with a negative test (unadjusted odds ratio [OR] 1.79 [95% CI, 1.30–2.47]). In multivariable analyses adjusted for demographics, comorbidities, laboratory values, and severity-of-illness characteristics, PF4 test positivity remained associated with a higher risk of severe AKI (adjusted OR 1.56 [95% CI 1.10–2.20]).

Conclusions: 
Among hospitalized adults, the presence of PF4 antibodies is independently associated with a 56% higher odds of developing severe AKI. Additional studies are needed to investigate potential mechanisms that may underlie these findings, such as pathogenic effects of PF4 antibodies on the microvasculature of the kidneys.
DOI: 10.17632/k57fr854j2.1
title: Lumbar Spine MRI Dataset

description: Our original data set contains clinical MRI study, or a set of scans, of 515 patients with symptomatic back pains. Each patient data can have one or more MRI studies associated with it. Each study contains slices, i.e., individual images taken from either sagittal or axial view, of the lowest three vertebrae and the lowest three IVDs. The axial view slices are mainly taken from the last three IVDs – including the one between the last vertebrae and the sacrum. The orientation of the slices of the last IVD are made to follow the spine curve whereas those of the other IVDs are usually made in blocks – i.e., parallel to each other. There are between four to five slices per IVD and they begin from the top of the IVD towards its bottom. Many of the top and bottom slices cut through the vertebrae leaving between one to three slices that cut the IVD cleanly and show purely the image of that IVD. In most cases, the total number of slices in axial view ranges from 12 to 15. However, in some cases, there may be up to 20 slices because the study contains slices of more than last three vertebrae. The scans in sagittal view also vary but all contain at least the last seven vertebrae and the sacrum. While the number of vertebrae varies, each scan always includes the first two sacral links. There are a total 48,345 MRI slices in our dataset. The majority of the slices have an image resolution of 320x320 pixels, however, there are slices from three studies with 320x310 pixel resolution. The pixels in all slices have 12-bit per pixel precision which is higher than the standard 8-bit greyscale images. Specifically for all axial-view slices, the slice thickness are uniformly 4 mm with centre-to-centre distance between adjacent slices to be 4.4 mm. The horizontal and vertical pixel spacing is 0.6875 mm uniformly across all axial-view slices. The majority of the MRI studies were taken with the patient in Head-First-Supine position with the rests were taken with the patient in in Feet-First-Supine position. Each study can last between 15 to 45 minutes and a patient may have one or more study associated with them taken at a different time or a few days apart.