cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19311613.v1
title: Additional file 2 of The patterns of co-occurrence variation are explained by the low dependence of bark beetles (Coleoptera: Scolytinae and Platypodinae) on hosts along altitude gradients

description: Additional file 2. Appendix file 1 showing a name list of total of 64,710 bark beetles from 264 species were collected from all three regions.
DOI: 10.6084/m9.figshare.19311614
title: Additional file 1 of Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

description: Additional file 1: Table S1. Results of yield estimation by machine learning algorithms.
DOI: 10.6084/m9.figshare.19311614.v1
title: Additional file 1 of Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.)

description: Additional file 1: Table S1. Results of yield estimation by machine learning algorithms.
DOI: 10.6084/m9.figshare.24680889
title: Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments

description: Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods—generalized estimating equations and linear mixed models—with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose efficient estimators for each estimand that allow for flexible covariate adjustment and additionally address cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed covariate-adjusted estimators, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimators are consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimators are triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24680889.v2
title: Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments

description: Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods—generalized estimating equations and linear mixed models—with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose efficient estimators for each estimand that allow for flexible covariate adjustment and additionally address cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed covariate-adjusted estimators, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimators are consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimators are triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19311616
title: Additional file 3 of The patterns of co-occurrence variation are explained by the low dependence of bark beetles (Coleoptera: Scolytinae and Platypodinae) on hosts along altitude gradients

description: Additional file 3. Appendix file 2 showing a name list of a total of 2184 tree individuals from 213 species from all three regions.
DOI: 10.6084/m9.figshare.24680889.v1
title: Model-robust and efficient covariate adjustment for cluster-randomized experiments

description: Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods—generalized estimating equations and linear mixed models—with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose efficient estimators for each estimand that allow for flexible covariate adjustment and additionally address cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed covariate-adjusted estimators, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimators are consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimators are triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
DOI: 10.6084/m9.figshare.19311616.v1
title: Additional file 3 of The patterns of co-occurrence variation are explained by the low dependence of bark beetles (Coleoptera: Scolytinae and Platypodinae) on hosts along altitude gradients

description: Additional file 3. Appendix file 2 showing a name list of a total of 2184 tree individuals from 213 species from all three regions.
DOI: 10.15468/7j4wd7
title: [SUIVI] 2017-2019 Ferme éolienne de Lichères Pres Aigremont SAS-Suivi 2017-2019 de l'avifaune hivernante (Projet éolien Ferme Lichère)

description: Suivi 2017-2019 de l'avifaune hivernante (Projet éolien Ferme Lichère)
DOI: 10.6084/m9.figshare.11971587.v1
title: PI3KC3 subunit NRBF2 is required for apoptotic cell clearance to restrict intestinal inflammation

description: NRBF2, a regulatory subunit of the ATG14-BECN1/Beclin 1-PIK3C3/VPS34 complex, positively regulates macroautophagy/autophagy. In this study, we report that NRBF2 is required for the clearance of apoptotic cells and alleviation of inflammation during colitis in mice. NRBF2-deficient mice displayed much more severe colitis symptoms after the administration of ulcerative colitis inducer, dextran sulfate sodium salt (DSS), accompanied by prominent intestinal inflammation and apoptotic cell accumulation. Interestingly, we found that <i>nrbf2</i><sup>−/-</sup> mice and macrophages displayed impaired apoptotic cell clearance capability, while adoptive transfer of <i>Nrbf2</i><sup>+/+</sup> macrophages to <i>nrbf2</i><sup>−/-</sup> mice alleviated DSS-induced colitis lesions. Mechanistically, NRBF2 is required for the generation of the active form of RAB7 to promote the fusion between phagosomes containing engulfed apoptotic cells and lysosomes via interacting with the MON1-CCZ1 complex and regulating the guanine nucleotide exchange factor (GEF) activity of the complex. Evidence from clinical samples further reveals the physiological role of NRBF2 in maintaining intestinal homeostasis. In biopsies of UC patient colon, we observed upregulated NRBF2 in the colon macrophages and the engulfment of apoptotic cells by NRBF2-positive cells, suggesting a potential protective role for NRBF2 in UC. To confirm the relationship between apoptotic cell clearance and IBD development, we compared TUNEL-stained cell counts in the UC with UC severity (Mayo Score) and observed a strong correlation between the two indexes, indicating that apoptotic cell population in colon tissue correlates with UC severity. The findings of our study reveal a novel role for NRBF2 in regulating apoptotic cell clearance to restrict intestinal inflammation.
DOI: 10.15468/h5kezp
title: Centrale photovoltaïque au sol de Thiaville-sur-Meurthe-Inventaire faune - Thiaville sur Meurthe - Parc photovoltaïque

description: Inventaires de la faune réalisés dans le cadre d'une étude d'impacts pour un projet de parc photovoltaïque sur la commune de Thiaville sur Meurthe (54). Taxons inventoriés : oiseaux, mammifères (hors chiroptères), chiroptères, insectes.
DOI: 10.15468/b7yaac
title: Pollen profile from a swamp at Seelohe, Fichtelgebirge, Germany
DOI: 10.15468/3tz2qk
title: Parc éolien Fache (Haudicourt)-faune fache

description: inventaire faune / parc éolien de 13 éoliennes
DOI: 10.17632/276f78wzsw.1
title: Quadrature of functions with endpoint singular and generalised polynomial behaviour in computational physics

description: Fast and accurate numerical integration always represented a bottleneck in high-performance computational physics, especially in large and multiscale industrial simulations involving Finite (FEM) and Boundary Element Methods (BEM). The computational demand escalates significantly in problems modelled by irregular or endpoint singular behaviours which can be approximated with generalised polynomials of real degree. This is due to both the practical limitations of finite-arithmetic computations and the inefficient samples distribution of traditional Gaussian quadrature rules. We developed a non-iterative mathematical software implementing an innovative numerical quadrature which largely enhances the precision of Gauss-Legendre formulae (G-L) for integrands modelled as generalised polynomial with the optimal amount of nodes and weights capable of guaranteeing the required numerical precision. This methodology avoids to resort to more computationally expensive techniques such as adaptive or composite quadrature rules. From a theoretical point of view, the numerical method underlying this work was preliminary presented in [1] by constructing the monomial transformation itself and providing all the necessary conditions to ensure the numerical stability and exactness of the quadrature up to machine precision. The novel contribution of this work concerns the optimal implementation of said method, the extension of its applicability at run-time with different type of inputs, the provision of additional insights on its functionalities and its straightforward implementation, in particular FEM applications or other mathematical software either as an external tool or embedded suite. The open-source, cross-platform C++ library Monomial Transformation Quadrature Rule (MTQR) has been designed to be highly portable, fast and easy to integrate in larger codebases. Numerical examples in multiple physical applications showcase the improved efficiency and accuracy when compared to traditional schemes.
DOI: 10.17632/83xrdht7mz.1
title: ERSN-OpenMC-Py: A python-based open-source software for OpenMC Monte Carlo code

description: The graphical user interface is a key element in facilitating the use of complex simulation software. This project describes the development of a graphical user interface called “ERSN-OpenMC-Py” for an existing neutron simulation code, OpenMC. The main goal is to make simulation more accessible to a wider audience by providing a user-friendly and intuitive user interface. The process of developing the graphical user interface is described in detail, including the different stages of development such as user interface design, user interface implementation, and user interface integration with the OpenMC simulation code. The development tools used, such as Python3 and PyQt5, are also explained. The user interface allows the user to control the simulation parameters and interact with the simulation results. Key features of the user interface include visualization of simulation results, modification of simulation parameters, saving and loading simulation configurations, as well as managing output files. The end result is a functional user interface that allows users to easily visualize simulation results and control simulation parameters in an intuitive manner. This user interface also provides a better user experience for non-programming experts who wish to use the simulation code for their own projects.
DOI: 10.17632/6fsmzp6srg.1
title: A SPIRED code for the reconstruction of spin distribution

description: In Nuclear Magnetic Resonance (NMR), it is of crucial importance to have an accurate knowledge of the spin probability distribution corresponding to inhomogeneities of the magnetic fields. An accurate identification of the sample distribution requires a set of experimental data that is sufficiently rich to extract all fundamental information. These data depend strongly on the control fields (and their number) used experimentally to perturb the spin system. In this work, we present and analyze a greedy reconstruction algorithm, and provide the corresponding SPIRED code, for the computation of a set of control functions allowing the generation of data that are appropriate for the accurate reconstruction of a sample probability distribution. In particular, the focus is on NMR and spin dynamics governed by the Bloch system with inhomogeneities in both the static and radio-frequency magnetic fields applied to the sample. We show numerically that the algorithm is able to reconstruct non trivial joint probability distributions of the two inhomogeneous Hamiltonian parameters. A rigorous convergence analysis of the algorithm is also provided.
DOI: 10.17632/8y7gcsm9j6.1
title: Exploratory_MCD_files_1

description: MCD files from image mass cytometry data in MEMORI cohort
DOI: 10.17632/49c35krw2n.1
title: Shi Hao, et al. c-KIT mediated autophagy suppression driven by a viral oncoprotein emerges as a crucial survival mechanism in Merkel cell carcinoma_Additional data

description: The uncropped WB images and source data for quantification
DOI: 10.17632/49c35krw2n.2
title: Shi Hao, et al. c-KIT mediated autophagy suppression driven by a viral oncoprotein emerges as a crucial survival mechanism in Merkel cell carcinoma_Additional data

description: The uncropped WB images and source data for quantification
DOI: 10.6084/m9.figshare.16655712
title: Additional file 4 of Genome-wide identification and characterization of the BES/BZR gene family in wheat and foxtail millet

description: Additional file 4.
DOI: 10.17632/4ck6jf5vxf.2
title: micrOMEGAs5.0: Freeze-in

description: We present a major upgrade of the micrOMEGAs dark matter code to compute the abundance of feebly interacting dark matter candidates through the freeze-in mechanism in generic extensions of the Standard Model of particle physics. We develop the necessary formalism in order to solve the freeze-in Boltzmann equations while making as few simplifying assumptions as possible concerning the phase-space distributions of the particles involved in the dark matter production process. We further show that this formalism allows us to treat different freeze-in scenarios and discuss the way it is implemented in the code. We find that, depending on the New Physics scenario under consideration, the effect of a proper treatment of statistics on the predicted dark matter abundance can range from a few percent up to a factor of two, or more. We moreover illustrate the underlying physics, as well as the various novel functionalities of micrOMEGAs, by presenting several example results obtained for different dark matter models.
DOI: 10.5066/p9qes2kj
title: Frequency Domain Electromagnetic (FDEM) Geophysical Data Collected near Lake Placid, NY

description: This data release contains frequency domain electromagnetic (FDEM) data collected during discrete times between July 2019 to October 2022 near Lake Placid, NY as part of a multi-method effort to monitor the effects of different road salt treatments on groundwater and the surrounding surface water (West Branch Ausable River). The FDEM instrument used was a Geophex GEM-2; a broadband sensor that measures the bulk conductivity and magnetic susceptibility of the subsurface down to approximately 5 meters (16.4 feet) depth. The instrument is hand carried by a single operator and data are collected at walking speeds. Data were repeatedly collected during each campaign at the three sites nicknamed for the planned road salt treatment at the site (i.e., “abrasives", "low salt", and "control"). Specifically, "control" refers to normal levels of salt applied in winter, "low salt" refers to a planned reduction in the amount of salt, and "abrasives" refers to an alternative sand/salt mix treatment. Limited data were also collected in August and October 2020 at the High Falls Gorge parking area ("high falls") and an area with an upgradient (relative to the road) spring ("springs"), respectively. All data were collected at 6 frequencies ranging from approximately 3KHz to 93KHz. Lower frequencies are sensitive to larger volumes of subsurface material, and therefore, have a deeper depth of investigation than higher frequencies (at the cost of an overall lower signal/noise ratio relative to higher frequencies). Data were collected in a horizontal coplanar orientation at walking speeds with the GEM-2 ~1 meter above ground surface. Surveys at each site include discrete sections of roadway (NY Route 86 and NY Route 73) and areas upgradient and downgradient from the roads. These data are in two zipped folders: (1) raw instrument data from each site with the associated time of collection (Raw.zip), and (2) compiled processed data converted to apparent electrical conductivity and magnetic susceptibility (Processed.zip).
DOI: 10.15468/f9yure
title: Parc éolien Source de la Sensée-Suivi de mortalité parc éolien Source de la Sensée 2022

description: Prospection pour 3 éoliennes du parc dans un rayon de 50m à la recherches de cadavres d'oiseux et chiroptères de la semaine 20 à 43, par TAUW France
DOI: 10.17632/4ck6jf5vxf.1
title: Collider limits on new physics within micrOMEGAs

description: Results from the LHC put severe constraints on models of new physics. This includes constraints on the Higgs sector from the precise measurement of the mass and couplings of the 125 GeV Higgs boson, as well as limits from searches for other new particles. We present the procedure to use these constraints in micrOMEGAs by interfacing it to the external codes Lilith, HiggsSignals, HiggsBounds and SModelS. A few dedicated modules are also provided. With these new features, micrOMEGAs_4.3.4 provides a generic framework for evaluating dark matter observables together with collider and non-collider constraints.
DOI: 10.17632/4ck6jf5vxf.3
title: micrOMEGAs 6.0: N-component dark matter

description: micrOMEGAs is a numerical code to compute dark matter (DM) observables in generic extensions of the Standard Model (SM) of particle physics. We present a new version of micrOMEGAs that includes a generalization of the Boltzmann equations governing the DM cosmic abundance evolution which can be solved to compute the relic density of N-component DM. The direct and indirect detection rates in such scenarios take into account the relative contribution of each component such that constraints on the combined signal of all DM components can be imposed. The co-scattering mechanism for DM production is also included, whereas the routines used to compute the relic density of feebly interacting particles have been improved in order to take into account the effect of thermal masses of t-channel particles. Finally, the tables for the DM self-annihilation - induced photon spectra have been extended down to DM masses of 110 MeV, and they now include annihilation channels into light mesons.
DOI: 10.6084/m9.figshare.19311629
title: Additional file 10 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

description: Additional file 10. The raw data from TCGA asnew raw data and data related to biochemical recurrence of prostate cancerpatients. There are three sheets in this file, the firstsheet “TCGA raw data” was the data directly obtained from TCGA dataset. In thesecond sheet “BCR state and time” we included data types we used for study.After excluding patients with missing “A8_New_Event_Time” data,we obtained 464 patients in sheet “data of464 patients”. BCR: Biochemical recurrence.
DOI: 10.6084/m9.figshare.16655712.v1
title: Additional file 4 of Genome-wide identification and characterization of the BES/BZR gene family in wheat and foxtail millet

description: Additional file 4.
DOI: 10.6084/m9.figshare.19311629.v1
title: Additional file 10 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

description: Additional file 10. The raw data from TCGA asnew raw data and data related to biochemical recurrence of prostate cancerpatients. There are three sheets in this file, the firstsheet “TCGA raw data” was the data directly obtained from TCGA dataset. In thesecond sheet “BCR state and time” we included data types we used for study.After excluding patients with missing “A8_New_Event_Time” data,we obtained 464 patients in sheet “data of464 patients”. BCR: Biochemical recurrence.
DOI: 10.17632/jb2784hvxp.1
title: Revisiting the Relation between Financial Inclusion and Economic Growth: A Global Analysis Using Panel Threshold Regression

description: Data and codes used for empirical analyses of this paper
DOI: 10.17632/cnxs8ztzsk.1
title: Supplementary Material - The geochemical evolution of basalt Enhanced Rock Weathering systems quantified from a natural analogue

description: This dataset contains supplementary material accompying the manuscript on "The geochemical evolution of basalt Enhanced Rock Weathering systems quantified from a natural analogue". 
The dataset includes the following: 1) measured soil water chemistry, 2) additions to the thermodynamic database and 3) the chemical composition of the rainwater used for the modelling, 4) temporal evolution of the soil water chemistry, 5) the redox values different redox couples in comparison to the measured data, 6) the calculations of the surface normalized dissolution rate of basalt.
DOI: 10.17632/cnxs8ztzsk.2
title: Supplementary Material - The geochemical evolution of basalt Enhanced Rock Weathering systems quantified from a natural analogue

description: This dataset contains supplementary material accompying the manuscript on "The geochemical evolution of basalt Enhanced Rock Weathering systems quantified from a natural analogue". https://doi.org/10.1016/j.gca.2024.02.005 
The dataset includes the following: 1) measured soil water chemistry, 2) additions to the thermodynamic database and 3) the chemical composition of the rainwater used for the modelling, 4) temporal evolution of the soil water chemistry, 5) the redox values different redox couples in comparison to the measured data, 6) the calculations of the surface normalized dissolution rate of basalt.
DOI: 10.6084/m9.figshare.19311628
title: Additional file 1 of Tenascin-C promotes bladder cancer progression and its action depends on syndecan-4 and involves NF-κB signaling activation

description: Additional file 1.
DOI: 10.6084/m9.figshare.19311628.v1
title: Additional file 1 of Tenascin-C promotes bladder cancer progression and its action depends on syndecan-4 and involves NF-κB signaling activation

description: Additional file 1.