cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31211931.v1
title: Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates

description: Characterizing the association between survival time and the dynamic patterns of a longitudinal covariate trajectory is of particular interest in many studies. Classical time-dependent survival models focus mainly on the link between the concurrent covariate value and the instantaneous hazard function. Consequently, the conditional survival function is often not properly defined on the whole time range, which causes difficulty in model estimation and interpretation. In this article, we propose a novel semiparametric joint modeling approach, in which the observed longitudinal trajectory is modeled as a random realization of a latent functional pattern. We assume each latent pattern uniquely indexes a global survival function via a log-linear functional regression model. Because the observational time interval of the longitudinal data depends on the survival time, we propose to jointly model the longitudinal and survival data. By using the latent pattern as an infinite-dimensional shared parameter, our approach extends the classical parametric joint modeling method to a semiparametric setting. We show that the proposed estimator achieves the semiparametric efficiency bound. Simulation studies and a real data application demonstrate the advantageous finite sample performances of our new approach.
DOI: 10.6084/m9.figshare.31211935
title: Enhanced inference for distributions and quantiles of individual treatment effects in various experiments

description: Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, despite they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect distributions and quantiles in both completely randomized and stratified randomized experiments, but they can be overly conservative by assuming the worst-case scenario where units with large effects are all assigned to the treated (or control) group. We introduce two improved methods to enhance the power of these existing approaches. The first method reinterprets existing approaches as inferring treatment effects among only treated or control units, and then combines the inference for treated and control units to infer treatment effects for all units. The second method explicitly controls for the actual number of treated units with large effects. Both simulation and application demonstrate the substantial gain from the improved methods. These methods are further extended to sampling-based experiments as well as quasi-experiments from matching, in which the ideas for both improved methods can play critical and complementary roles.
DOI: 10.6084/m9.figshare.31211935.v1
title: Enhanced inference for distributions and quantiles of individual treatment effects in various experiments

description: Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, despite they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect distributions and quantiles in both completely randomized and stratified randomized experiments, but they can be overly conservative by assuming the worst-case scenario where units with large effects are all assigned to the treated (or control) group. We introduce two improved methods to enhance the power of these existing approaches. The first method reinterprets existing approaches as inferring treatment effects among only treated or control units, and then combines the inference for treated and control units to infer treatment effects for all units. The second method explicitly controls for the actual number of treated units with large effects. Both simulation and application demonstrate the substantial gain from the improved methods. These methods are further extended to sampling-based experiments as well as quasi-experiments from matching, in which the ideas for both improved methods can play critical and complementary roles.
DOI: 10.5517/ccdc.csd.cc289thx
title: CCDC 2094809: Experimental Crystal Structure Determination

description: Related Article: Hadi Kargar, Mehdi Fallah-Mehrjardi, Reza Behjatmanesh-Ardakani, Hadi Amiri Rudbari, Amir Adabi Ardakani, Samaneh Sedighi-Khavidak, Khurram Shahzad Munawar, Muhammad Ashfaq, Muhammad Nawaz Tahir|2022|Polyhedron|213|115636|doi:10.1016/j.poly.2021.115636
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc289tjy
title: CCDC 2094810: Experimental Crystal Structure Determination

description: Related Article: Hadi Kargar, Mehdi Fallah-Mehrjardi, Reza Behjatmanesh-Ardakani, Hadi Amiri Rudbari, Amir Adabi Ardakani, Samaneh Sedighi-Khavidak, Khurram Shahzad Munawar, Muhammad Ashfaq, Muhammad Nawaz Tahir|2022|Polyhedron|213|115636|doi:10.1016/j.poly.2021.115636
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5683/sp3/zanoij
title: Data for growth factor and pro-inflammatory cytokine levels in response to short chain fatty acid treatment in enteric glial cells

description: This is the raw qPCR and Western immunoblotting data set for a manuscript under review titled "Short Chain Fatty Acids Elicit Differential Expression of Growth Factors and Pro-inflammatory Cytokines in Enteric Glial Cells". The manuscript explains and contextualizes how butyrate and propionate modulate mRNA and protein levels of growth factors (glial cell line-derived neurotrophic factor and transforming growth factor beta) and pro-inflammatory cytokines (TNFalpha, IL-6, IL-1beta) in isolated enteric glial cells.
description: Added related publication.
DOI: 10.6084/m9.figshare.31211938
title: Variable Significance Testing for the Deep Cox Model

description: Deep learning has become enormously popular in the analysis of complex data, including event time measurements with censoring. To date, deep survival methods have mainly focused on prediction. Such methods are scarcely used in matters of statistical inference such as hypothesis testing. Due to their black-box nature, deep-learned outcomes lack interpretability which limits their use for decision-making in biomedical applications. This paper provides estimation and inference methods for the nonparametric Cox model – a flexible family of models with a nonparametric link function to avoid model misspecification. Here we assume the nonparametric link function is modeled via a deep neural network. To perform statistical inference, we utilize sample splitting and cross-fitting procedures to get neural network estimators and construct test statistic. These procedures enable us to propose a new significance test to examine the association of certain covariates with event times. We establish convergence rates of the neural network estimators, and show that deep learning can overcome the curse of dimensionality in nonparametric regression by learning to exploit low-dimensional structures underlying the data. In addition, we show that our test statistic converges to a normal distribution under the null hypothesis and establish its consistency, in terms of the Type II error, under the alternative hypothesis. Numerical simulations and a real data application demonstrate the usefulness of the proposed test.
DOI: 10.6084/m9.figshare.31211938.v1
title: Variable Significance Testing for the Deep Cox Model

description: Deep learning has become enormously popular in the analysis of complex data, including event time measurements with censoring. To date, deep survival methods have mainly focused on prediction. Such methods are scarcely used in matters of statistical inference such as hypothesis testing. Due to their black-box nature, deep-learned outcomes lack interpretability which limits their use for decision-making in biomedical applications. This paper provides estimation and inference methods for the nonparametric Cox model – a flexible family of models with a nonparametric link function to avoid model misspecification. Here we assume the nonparametric link function is modeled via a deep neural network. To perform statistical inference, we utilize sample splitting and cross-fitting procedures to get neural network estimators and construct test statistic. These procedures enable us to propose a new significance test to examine the association of certain covariates with event times. We establish convergence rates of the neural network estimators, and show that deep learning can overcome the curse of dimensionality in nonparametric regression by learning to exploit low-dimensional structures underlying the data. In addition, we show that our test statistic converges to a normal distribution under the null hypothesis and establish its consistency, in terms of the Type II error, under the alternative hypothesis. Numerical simulations and a real data application demonstrate the usefulness of the proposed test.
DOI: 10.6084/m9.figshare.31211943
title: Totally Concave Regression

description: Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods—except additive models—impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This paper introduces a novel multivariate shape-constrained regression approach based on total concavity, originally studied by T. Popoviciu. Our method allows interactions while mitigating the curse of dimensionality, with convergence rates that depend only logarithmically on the number of covariates. We characterize and compute the least squares estimator over totally concave functions, derive theoretical guarantees, and demonstrate its practical effectiveness through empirical studies on real-world datasets.
DOI: 10.6084/m9.figshare.31211943.v1
title: Totally Concave Regression

description: Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods—except additive models—impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This paper introduces a novel multivariate shape-constrained regression approach based on total concavity, originally studied by T. Popoviciu. Our method allows interactions while mitigating the curse of dimensionality, with convergence rates that depend only logarithmically on the number of covariates. We characterize and compute the least squares estimator over totally concave functions, derive theoretical guarantees, and demonstrate its practical effectiveness through empirical studies on real-world datasets.
DOI: 10.5281/zenodo.17960034
title: Data and Code: Safeguarding aerial migrants need not jeopardize wind energy production

description: This collection contains code and data for analysing weather-radar derived bird movements and potential wind energy production across Western Europe (Germany, France, Belgium, Luxembourg and The Netherlands).
DOI: 10.5281/zenodo.17960033
title: Data and Code: Safeguarding aerial migrants need not jeopardize wind energy production

description: This collection contains code and data for analysing weather-radar derived bird movements and potential wind energy production across Western Europe (Germany, France, Belgium, Luxembourg and The Netherlands).
DOI: 10.6084/m9.figshare.31211964
title: Towards Better Statistical Understanding of Watermarking LLMs

description: In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its asymptotic Pareto optimality between model distortion and detection ability. Such a result guarantees an averaged increased green list probability and henceforth detection ability explicitly (in contrast to previous results). Moreover, we provide a systematic discussion on the choice of the model distortion metrics for the watermarking problem. We justify our choice of KL divergence and present issues with the existing criteria of “distortion-free” and perplexity. Finally, we empirically evaluate our algorithms on extensive datasets against benchmark algorithms.
DOI: 10.6084/m9.figshare.31211964.v1
title: Towards Better Statistical Understanding of Watermarking LLMs

description: In this paper, we study the problem of watermarking large language models (LLMs). We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the red-green list watermarking algorithm. We show that the optimal solution to the optimization problem enjoys a nice analytical property which provides a better understanding and inspires the algorithm design for the watermarking process. We develop an online dual gradient ascent watermarking algorithm in light of this optimization formulation and prove its asymptotic Pareto optimality between model distortion and detection ability. Such a result guarantees an averaged increased green list probability and henceforth detection ability explicitly (in contrast to previous results). Moreover, we provide a systematic discussion on the choice of the model distortion metrics for the watermarking problem. We justify our choice of KL divergence and present issues with the existing criteria of “distortion-free” and perplexity. Finally, we empirically evaluate our algorithms on extensive datasets against benchmark algorithms.
DOI: 10.25345/c5qn5zr3c
title: MassIVE MSV000100664 - Adrenocortical uptake of nanosponges in the presence and absence of a protein corona
title: Serum proteomics analysis before and after incubation with nanosponges
DOI: 10.25824/redu/lijvpx
title: Dados da pesquisa “Formar-se entre palavras: a produção de narrativas do vivido nos estágios da Pedagogia”

description: Este conjunto de dados refere-se à pesquisa de doutorado intitulada “Formar-se entre palavras: a produção de narrativas do vivido nos estágios da Pedagogia”, desenvolvida junto à Faculdade de Educação da Universidade Estadual de Campinas (Unicamp), no ano de 2021, em contexto de ensino remoto durante a pandemia de Covid-19. Previamente ao início do trabalho de campo, o projeto foi submetido à apreciação do Comitê de Ética em Pesquisa com Seres Humanos da Unicamp e aprovado, assegurando que todas as etapas de produção, interpretação e uso do material empírico estivessem em conformidade com os princípios éticos vigentes. A participação das estudantes do curso de Pedagogia ocorreu de forma voluntária, mediante assinatura do Termo de Consentimento Livre e Esclarecido (TCLE). O estudo voltou-se à análise de textos produzidos por estudantes no âmbito das disciplinas de Práticas de Ensino e Estágio Supervisionado nos Anos Iniciais do Ensino Fundamental. Tais produções consistem, majoritariamente, em narrativas do vivido, compreendidas como escritos reflexivos nos quais as estudantes registram, descrevem e interpretam experiências desenvolvidas nos contextos escolares de estágio, articulando acontecimentos do cotidiano da escola, percepções pessoais e diálogos com distintos referenciais mobilizados ao longo da formação. O corpus empírico reúne 131 produções escritas, sendo 100 narrativas do vivido e 31 planos de atuação pedagógica, elaboradas por 32 participantes do curso de Pedagogia. Os planos de atuação correspondem a textos produzidos no âmbito do estágio que sistematizam propostas de intervenção ou de acompanhamento do trabalho pedagógico nas escolas. Considerando a presença de dados sensíveis, como referências a escolas, territórios, experiências pessoais e situações concretas do cotidiano escolar, e os compromissos éticos assumidos com as participantes, as produções integrais não são disponibilizadas publicamente, a fim de resguardar a confidencialidade das fontes e evitar possibilidades de identificação direta ou indireta. Com o objetivo de assegurar transparência metodológica e rastreabilidade do estudo, este conjunto de dados disponibiliza uma tabela com informações sobre a contribuição individual das participantes que autorizaram o uso de seus registros para fins de pesquisa. Para fins de identificação interna na tabela, foram utilizados nomes fictícios, atribuídos de forma aleatória, sem qualquer correspondência ou semelhança com os nomes reais das participantes, de modo a preservar integralmente o anonimato. O acesso aos documentos integrais permanece restrito, conforme os parâmetros éticos estabelecidos no projeto aprovado.
DOI: 10.5281/zenodo.18369061
title: A Multilingual Dataset of Student Answers, Human Grading, and Multi-LLM Evaluations for Automated Assessment Research using JorGPT

description: This is a multilingual dataset of open-ended student answers collected from authentic university-level assessments. The dataset comprises 3041 student responses to 50 distinct open-ended questions, authored by 79 anonymized students from a single institution across one semester of the academic course 2025-26. It integrates real student answers, instructor-defined ideal solutions, numerical grades and qualitative feedback provided by human teachers, and structured evaluations generated by multiple state-of-the-art LLMs following a unified grading schema.

All pedagogical content is available in both Spanish, the original language of instruction, and English, enabling multilingual and cross-lingual research. The dataset supports benchmarking of automated grading systems, analysis of alignment between human and LLM-based assessment, training of judge or meta-evaluation models, and studies on automated feedback generation. By releasing this dataset under an open license, we aim to facilitate transparent, reproducible, and realistic research in educational artificial intelligence and automated assessment.
DOI: 10.5281/zenodo.18434167
title: A Multilingual Dataset of Student Answers, Human Grading, and Multi-LLM Evaluations for Automated Assessment Research using JorGPT

description: This is a multilingual dataset of open-ended student answers collected from authentic university-level assessments. The dataset comprises 3041 student responses to 50 distinct open-ended questions, authored by 79 anonymized students from a single institution across one semester of the academic course 2025-26. It integrates real student answers, instructor-defined ideal solutions, numerical grades and qualitative feedback provided by human teachers, and structured evaluations generated by multiple state-of-the-art LLMs following a unified grading schema.

All pedagogical content is available in both Spanish, the original language of instruction, and English, enabling multilingual and cross-lingual research. The dataset supports benchmarking of automated grading systems, analysis of alignment between human and LLM-based assessment, training of judge or meta-evaluation models, and studies on automated feedback generation. By releasing this dataset under an open license, we aim to facilitate transparent, reproducible, and realistic research in educational artificial intelligence and automated assessment.
DOI: 10.5281/zenodo.18434178
title: LINF_320017500

description: Ankyrin repeat-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18434177
title: LINF_320017500

description: Ankyrin repeat-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.6084/m9.figshare.31211602
title: Supplementary tables

description: Haemorrhage is the leading preventable cause of trauma death, primarily through ischaemic consequences that current treatments cannot adequately address. We combined human transcriptomic data (n=458) with a controlled porcine model of haemorrhagic shock to identify treatment-responsive molecular mechanisms. Using latent factorisation, we prioritised distinct molecular signatures of the human shock response, including stress signalling, neutrophil activation, and cytotoxic lymphocyte programmes. We assessed the behaviour of these pathways in the experimental porcine system, revealing that shock-initiated immune trajectories are not immutable: blood resuscitation normalised maladaptive transcriptomic changes whilst noradrenaline exacerbated them. While resuscitation modulated neutrophil, heat-shock, and barrier defence programmes, interferon and coagulation pathways were neither mortality-predictive nor treatment-responsive. A factor representing p38-MAPK/AP-1 stress signalling emerged as the dominant mortality-predictive pathway. An in silico small molecule screen identified p38 inhibitors as leading candidates for reversing shock-induced transcriptomic signatures. Our framework identifies modifiable pathways in trauma shock, prioritising p38-MAPK inhibition for therapeutic development and providing a systematic approach for trauma drug repurposing.
DOI: 10.6084/m9.figshare.31211602.v1
title: Supplementary tables

description: Haemorrhage is the leading preventable cause of trauma death, primarily through ischaemic consequences that current treatments cannot adequately address. We combined human transcriptomic data (n=458) with a controlled porcine model of haemorrhagic shock to identify treatment-responsive molecular mechanisms. Using latent factorisation, we prioritised distinct molecular signatures of the human shock response, including stress signalling, neutrophil activation, and cytotoxic lymphocyte programmes. We assessed the behaviour of these pathways in the experimental porcine system, revealing that shock-initiated immune trajectories are not immutable: blood resuscitation normalised maladaptive transcriptomic changes whilst noradrenaline exacerbated them. While resuscitation modulated neutrophil, heat-shock, and barrier defence programmes, interferon and coagulation pathways were neither mortality-predictive nor treatment-responsive. A factor representing p38-MAPK/AP-1 stress signalling emerged as the dominant mortality-predictive pathway. An in silico small molecule screen identified p38 inhibitors as leading candidates for reversing shock-induced transcriptomic signatures. Our framework identifies modifiable pathways in trauma shock, prioritising p38-MAPK inhibition for therapeutic development and providing a systematic approach for trauma drug repurposing.