cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28868486.v7
title: <b>The impact of CD4 T cell heterogeneity on bladder cancer immunotherapy responsiveness</b>

description: In this study, we identify a novel population of CD4<sup>+</sup> T cells, which are characterized by memory-like activation markers and strongly correlate with therapeutic responses in bladder cancere identify a novel population of CD4<sup>+</sup> T cells, which are characterized by memory-like activation markers and strongly correlate with therapeutic responses in bladder cancer.
DOI: 10.6084/m9.figshare.10298039.v1
title: Dataset for JGR-Planets paper: Quantitative Characterization of Impact Crater Materials on the Moon: Topographic Roughness and Thermophysical Properties

description: We quantitatively characterized crater materials using 1) the topographic roughness calculated form LOLA, and 2) rock abundances and night-time soil temperatures derived from DIVINER measurements. We subdivided crater materials into central peak, floor, wall, and continuous ejecta, mapped 4770 crater-material subunits, and calculated the quantitative parameters for each of them. <br>
DOI: 10.6084/m9.figshare.15066696.v1
title: Supplementary Material for: Sarcopenic Obesity in Chronic Kidney Disease: Challenges in Diagnosis Using Different Diagnostic Criteria

description: <b><i>Objectives:</i></b> Obesity, muscle impairment (low muscle mass or strength), and sarcopenic obesity are present in chronic kidney disease (CKD) and are associated with worse clinical prognosis. However, the various existing definitions for these conditions make the diagnosis variable. The aim of the present study was to evaluate the agreement between diagnostic criteria for sarcopenic obesity and its components in CKD. <b><i>Subject and Methods:</i></b> 267 patients with CKD were included in the study. We assessed body composition by dual-energy X-ray absorptiometry and muscle function by handgrip strength (HGS) and adiposity by body mass index (BMI), waist circumference (WC), fat mass index (FMI), and percentage of FM. Diagnosis of muscle impairment was made by HGS, appendicular lean mass (ALM), and ALM index; obesity by BMI, WC, FMI, and %FM, and sarcopenic obesity was diagnosed by concomitant presence of muscle impairment and obesity. <b><i>Results:</i></b> Prevalence of muscle impairment varied from 11 to 50%, higher when low muscle mass criteria were used. Prevalence of obesity varied from 26 to 62%, higher when WC and %FM criteria were used. Prevalence of sarcopenic obesity varied from 2 to 23%. Women were more affected by sarcopenic obesity. Muscle impairment and sarcopenic obesity were more prevalent among patients on hemodialysis and obesity among nondialysis-dependent and kidney transplant patients. The agreement was poor between muscle mass and strength criteria; substantial between FMI, BMI, and %FM and fair between WC and the other measures; for sarcopenic obesity, it varied from poor to almost perfect. <b><i>Conclusion:</i></b> Significant differences were found among the various diagnostic criteria that are used in the diagnosis of sarcopenic obesity. Our results highlight the need for standardization in the diagnosis of sarcopenic obesity.
DOI: 10.6084/m9.figshare.20310336.v1
title: Supplementary Material for: Amnioinfusion versus usual care in women with prelabour rupture of membranes in midtrimester: a systematic review and meta-analysis of short and long-term outcomes

description: Introduction: Midtrimester prelabour rupture of membranes (PROM) between 16 and 24 weeks’ gestational age is a major obstetric complication with high rates of perinatal morbidity and mortality. Amnioinfusion has been proposed in women with midtrimester PROM to target oligohydramnios and subsequently enhance pulmonary development and perinatal outcomes. Material and methods: The purpose of this study was to perform a systematic review and meta-analysis including all randomized clinical trials investigating amnioinfusion versus no intervention in women with PROM between 16+0 and 24+0 weeks gestational age. Databases Central, Embase, Medline, ClinicalTrials.gov and references of identified articles were searched from inception of database to December 2021. The primary outcome was perinatal mortality. Secondary outcomes included neonatal, maternal and long-term developmental outcomes as defined in the core outcome set for preterm birth studies. Summary measures were reported as pooled relative risk (RR) or mean difference (MD) with corresponding 95% of confidence interval (CI). Results: Two studies (112 patients, 56 in the amnioinfusion group and 56 in the no intervention group) were included in this review. Pooled perinatal mortality was 66.1% (37/56) in the amnioinfusion group compared with 71.4% (40/56) in no intervention group (RR 0.92, 95% CI 0.72 - 1.19). Other neonatal and maternal core outcomes were similar in both groups although, due the relatively small number of events and wide confidence intervals, there is possibility that amnioinfusion can be associated with clinically important benefits and harms. Long-term healthy survival was seen in 35.7% (10/28) of children assessed for follow-up and treated with amnioinfusion versus 28.6% (8/28) after no intervention (RR 1.30, 95% CI 0.47 - 3.60, ‘best case scenario’). Conclusions: Based on these findings the benefits of amnioinfusion for midtrimester PROM &lt;24 weeks gestational age are unproven and the potential harms remain undetermined.
DOI: 10.6084/m9.figshare.3553752.v1
title: Appendix C. A figure showing the effect of tree root removal on dissolved inorganic nitrogen to dissolved organic nitrogen ratios in small, medium, and large island size classes.

description: A figure showing the effect of tree root removal on dissolved inorganic nitrogen to dissolved organic nitrogen ratios in small, medium, and large island size classes.
DOI: 10.6084/m9.figshare.12687908.v1
title: Maximum constraint on the response of terrestrial productivity to precipitation variability

description: Code and datasets used in the article "Maximum constraint on the response of terrestrial productivity to precipitation variability"<br>
DOI: 10.6084/m9.figshare.19775548.v1
title: Descriptive data of pretest and posttest measurements.

description: Socio-environmental conflicts arise due to disputes over access to resources in the territories, which is usually exacerbated by pollution problems and even more so by climate change effects on the environment, such as intense droughts, water shortages or river overflows. The current investigation analyzes precisely the effect that a natural disaster, specifically a flood, had on a population of Sucre, Colombia, where there were constant socio-environmental conflicts due to their attitudes and behaviors, before the Pansegüita river and the way of relating to the communities. other people in the community. The sample consisted of 400 adults who lived on the boundaries of the water resource, with an average age of 45.7 years, to whom the Socio-environmental Conflict Identification Scale (ICS-ENVIRONMENTAL) was applied before and after the flood. The effect of this event is analyzed using the Wilcoxon statistical test, approving the research hypothesis, finding a statistically significant difference p=0.000) before and after flooding, for all the dimensions evaluated: use of water, land and in waste management. Finally, conclusions and practical implications of the current research are expressed.. Socio-environmental conflicts arise due to disputes over access to resources in the territories, which is usually exacerbated by pollution problems and even more so by climate change effects on the environment, such as intense droughts, water shortages or river overflows. The current investigation analyzes precisely the effect that a natural disaster, specifically a flood, had on a population of Sucre, Colombia, where there were constant socio-environmental conflicts due to their attitudes and behaviors, before the Pansegüita river and the way of relating to the communities. other people in the community. The sample consisted of 400 adults who lived on the boundaries of the water resource, with an average age of 45.7 years, to whom the Socio-environmental Conflict Identification Scale (ICS-ENVIRONMENTAL) was applied before and after the flood. The effect of this event is analyzed using the Wilcoxon statistical test, approving the research hypothesis, finding a statistically significant difference p=0.000) before and after flooding, for all the dimensions evaluated: use of water, land and in waste management. Finally, conclusions and practical implications of the current research are expressed..
DOI: 10.6084/m9.figshare.28601087.v1
title: Supplementary Material 9

description: CD-HIT (Cluster Database at High Identity with Tolerance) is a widely used clustering algorithm that reduces redundancy in large genomic datasets. CD-HIT can group similar sequences or genomic features when applied to machine learning results in Escherichia coli genomic analysis, improving model efficiency and reducing computational complexity.<b>CD-HIT in machine learning-based </b><b><i>E. coli</i></b><b> genomic analysis:</b><b>Feature reduction:</b> In supervised machine learning, CD-HIT can cluster similar sequences from genomic data, eliminating redundant information and improving feature selection for classifiers like Random Forest, XGBoost, and SVM.<b>Antimicrobial resistance (AMR) analysis:</b> By clustering resistance gene sequences at high identity thresholds (e.g., 90% or 95%), CD-HIT helps identify unique resistance patterns while reducing data redundancy.<b>Virulence gene clustering:</b> CD-HIT can be applied to virulence factor datasets to cluster homologous genes, aiding in classifying pathogenic <i>E. coli</i> strains.<b>Improving model generalization:</b> Reducing sequence redundancy prevents overfitting in machine learning models, leading to better generalization in predicting resistant and susceptible <i>E. coli</i> strains.<b>Computational efficiency:</b> CD-HIT optimizes data processing by clustering sequences before applying machine learning models, making large-scale genomic analyses more feasible.<b>CD-HIT results interpretation:</b><b>High identity clustering (e.g., 95-100%)</b>: Groups highly similar sequences, ensuring that only distinct genetic variations contribute to machine learning predictions.<b>Lower identity clustering (e.g., 70-90%)</b> Groups more diverse sequences, which is helpful for broader strain classification or identifying distant homologs in resistance genes.CD-HIT results help refine machine learning predictions in <i>E. coli</i> genomic analysis by ensuring that training data remains diverse and balanced.
DOI: 10.6084/m9.figshare.5110726.v1
title: Fraxinus angustifolia chorology

description: Distribution map of narrow-leafed ash (<i>Fraxinus angustifolia</i>)<br><br>These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geodatabases and scientific literature.<br>The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix “plg”), which define continuous areas of occupancy of the species, and with point features (name suffix “pnt”), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix “syn”).<br>All polygon borders delimiting the range are generalized across the mainland and sea boundaries, and not clipped to a specific coastline. This offers the possibility to mask sea areas, or to clip and extract the terrestrial range parts using GIS data layers of the users’ choice. <br><br>Please cite as:<br>Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007 <br><br>Additional information and used references are on 'supplementary materials' document:<br>https://doi.org/10.6084/m9.figshare.5091901<br><br>Chorological maps are part of the "European Atlas of Forest Tree Species" project:<br>https://w3id.org/mtv/FISE-Comm/v01
DOI: 10.6084/m9.figshare.644497.v5
title: Animated GIFs

description: A group of animated GIFs developed to hopefully explain natural phenomena
DOI: 10.17632/973crf9hgd
title: NESSi: The Non-Equilibrium Systems Simulation package

description: The nonequilibrium dynamics of correlated many-particle systems is of interest in connection with pump-probe experiments on molecular systems and solids, as well as theoretical investigations of transport properties and relaxation processes. Nonequilibrium Green’s functions are a powerful tool to study interaction effects in quantum many-particle systems out of equilibrium, and to extract physically relevant information for the interpretation of experiments. We present the open-source software package NESSi (The Non-Equilibrium Systems Simulation package) which allows to perform many-body dynamics simulations based on Green’s functions on the L-shaped Kadanoff-Baym contour. NESSi contains the library libcntr which implements tools for basic operations on these nonequilibrium Green’s functions, for constructing Feynman diagrams, and for the solution of integral and integro-differential equations involving contour Green’s functions. The library employs a discretization of the Kadanoff-Baym contour into time N points and a high-order implementation of integration routines. The total integrated error scales up to O(N^-7), which is important since the numerical effort increases at least cubically with the simulation time. A distributed-memory parallelization over reciprocal space allows large-scale simulations of lattice systems. We provide a collection of example programs ranging from dynamics in simple two-level systems to problems relevant in contemporary condensed matter physics, including Hubbard clusters and Hubbard or Holstein lattice models. The libcntr library is the basis of a follow-up software package for nonequilibrium dynamical mean-field theory calculations based on strong-coupling perturbative impurity solvers.
DOI: 10.6084/m9.figshare.28122740.v1
title: brGDGTs in Mangshan and Jiyuan

description: brGDGTs-derived land surface temperatures from Mangshan and Jiyuan sections in the CLP
DOI: 10.6084/m9.figshare.28452545.v3
title: The iToBoS Dataset: Skin Region Images Extracted from 3D Total Body Photographs for Lesion Detection

description: <b>Abstract:</b><br>The early detection of skin cancer is critical for improving patient outcomes. Traditionally, dermatologists rely on dermoscopy to examine pigmented skin lesions. While this non-invasive technique enhances diagnostic accuracy, its effectiveness is highly dependent on the clinician’s expertise. Additionally, capturing dermoscopic images for every suspicious lesion is a labor-intensive process. Given these challenges, there is an increasing need for computer-aided diagnosis (CAD) systems that utilize conventional cameras. Such systems can support general physicians and other non-specialist practitioners in identifying potential malignant lesion, improving early detection and intervention. Moreover, they facilitate longitudinal tracking of lesions, aiding researchers in studying disease progression and treatment efficacy.This dataset provides high-resolution skin patch images extracted from 3D total body photographs to support the development of advanced machine learning models for lesion detection. It serves as a valuable resource for researchers working on automated skin lesion analysis, particularly in the context of total body photography (TBP).<b>Dataset Description:</b><br>The iToBoS dataset consists of 16,954 high-resolution images of skin regions obtained from anonymized 3D avatars of patients. These avatars were generated using the Canfield VECTRA WB360 system, a cutting-edge imaging technology that captures comprehensive, full-body skin images using 92 fixed cameras arranged in 46 stereo pairs with xenon flash lighting. The images were collected from patients at two clinical sites: the Clinical Hospital of Barcelona (Spain) and the University of Queensland (Australia).The dataset provides diverse anatomical locations, including the torso, arms, and legs, with each image having an average resolution of 1012x827 pixels and a 45-pixel overlap between adjacent images. The images are extracted from 3D avatars while ensuring compliance with GDPR regulations by automatically removing patient facial features. Each image is accompanied by metadata, including patient age range, body location, and sun damage score, allowing for in-depth analysis and stratification.<b>Significance of the Dataset:</b><b>Facilitates Automated Skin Lesion Detection:</b> The dataset supports the development of AI-based lesion detection models that can improve early diagnosis of skin cancer, particularly in regions with limited access to dermatological expertise.<b>Enables Longitudinal Skin Analysis:</b> By capturing multiple scans from patients, researchers can track lesion evolution over time and assess treatment effectiveness.<b>Enhances Machine Learning Applications:</b> The dataset serves as a benchmark for developing state-of-the-art computer vision and deep learning models for detection of skin lesions.<br>
DOI: 10.6084/m9.figshare.28943015.v1
title: Raw data for the paper "A Terahertz Point Source Meta-Sensor in Reflection Mode for Trace-Amount Bio-Sensing Applications"

description: Biosensors in the Terahertz (THz) region are attracting significant attention in the biomedical and chemical analysis fields owing to their potential for ultra-trace sensing of various solutions with high sensitivity. However, the development of compact, highly sensitive chips and methods for easy, rapid, and trace-amount measurements have been significantly hindered by the limited spatial resolution of THz waves and their strong absorption by water. In this study, we developed a nonlinear optical crystal (NLOC)-based compact THz sensor chip, and a near-field point THz source with a diameter of ~ϕ20 μm was locally generated via optical rectification. Here, only the single central meta-atom was excited. The reflective resonance responses highly depend on the array number and period of the meta-atom structures. The sensing performance was examined with several liquid biological samples, such as mineral water, DNA, and human blood. 1 μL of samples was directly dropped onto the meta-surface with an effective sensing area of 0.32 mm<sup>2</sup> (564 μm × 564 μm). Obvious resonance frequency shifts were clearly observed. This research holds significance in advancing liquid bio-sample sensing methodologies by facilitating easy, rapid, and trace-amount measurements and promoting the development of compact and highly sensitive THz sensors tailored for liquid biological samples.
DOI: 10.17632/5ct875rx9c
title: Cloud Attack Dataset For Building Machine Learning and Deep Learning Models

description: The cloud attacks dataset is prepared for evaluating the performance of machine learning and deep learning models which are built and designed to detect network attacks. The traffic dataset is provided with training and testing sets. The training set consists of 2500 Benign (normal) traffic instances and 2500 DDoS attack traffic instances in Binary visualization form suitable for building deep learning models. The test set consists of 584 Benign traffic instances and 584 DDoS attack traffic instances in image representation.  Thus, the dataset consists of traffic instances categorized into two classes (i) Benign traffic and (ii) DDoS Attack traffic. The dataset is a balanced dataset. This dataset is obtained by considering the traffic instances from the CICIDS 2019 dataset which provides various categories of modern network attacks along with Benign traffic instances. Interested researchers can also refer to the IoT DoS DDoS Attack dataset which is an imbalanced dataset generated using CICIDS 2019 dataset. 

The dataset provided here can be used to test the performance of advanced machine learning algorithms and deep learning models  aimed at modern network attacks detection in Cloud and IoT environments.
DOI: 10.17632/dmvntgxscm
title: Inverse problem for quadratic differential pencil: numerical examples

description: This data set contains the program code in MATLAB/GNU Octave for obtaining numerical examples in [1]. The data generated by this code are also included. The theoretical description of the method and of the initial data are provided in [1]. [1] Bondarenko, N.P.; Gaidel, A.V. Solvability and stability of the inverse problem for the quadratic differential pencil, arXiv:2103.09802 [math.SP]
DOI: 10.17632/sxybkgc5xc
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/xs6mvhx6rh
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: 10.6084/m9.figshare.24452773.v3
title: Hainan_Historical_Areca_palm_distribution_from_1987_to_2022

description: In order to produce the areca plam distribution , we used data fusion and combined deep learning with machine learning techniques to map the geographical spatial distribution of areca palms on Hainan Island, China’s largest plantation area, from 1987 to 2022.Note: Please cite the article below if relevant data is involved.Wang, C.; Yin, Z.; Luo, R.; Qian, J.; Fu, C.; Wang, Y.; Xie, Y.; Liu, Z.; Qiu, Z.; Pei, H. Spatiotemporal Evolution and Impact Mechanisms of Areca Palm Plantations in China (1987–2022). <i>Forests</i> <b>2024</b>, <i>15</i>, 1679. https://doi.org/10.3390/f15101679