cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30698230
title: Living in ice: Examining the effects of temperature on thermal and metabolic physiology of glacier ice worms (<i>Mesenchytraeus solifugus</i>)

description: Animals employ many strategies to survive in extreme cold. Glacier ice worms (<i>Mesenchytraeus solifugus</i>) are the largest animals that spend their entire life cycle in ice. Indeed, they spend most of their lives near 0°C and accumulate adenosine trisphosphate (ATP) to mitigate the impacts of cold on their performance. However, the degree to which ice worms can survive temperatures above and below freezing has not been thoroughly investigated. Furthermore, the role of cold temperatures in shaping whole-body metabolism in ice worms is largely unknown. Here, we assessed thermal limits of ice worms as well as their whole-body metabolic rates. Notably, we found that though ice worms can survive short-term exposures to surprisingly warm temperatures (~26°C), they cannot tolerate freezing, including internal ice formation. Ice worm metabolic rates also significantly increased with temperature up to 16°C where a significant break point occurred. Taken together, our results further illuminate how ice worms survive their unique life in ice and highlight how close they live to their lower thermal limits. Looking ahead, we note the clear risks that anthropogenic climate change, glacier recession, and loss of mountain snowpack pose to the future of ice worms.
DOI: 10.6084/m9.figshare.30698230.v1
title: Living in ice: Examining the effects of temperature on thermal and metabolic physiology of glacier ice worms (<i>Mesenchytraeus solifugus</i>)

description: Animals employ many strategies to survive in extreme cold. Glacier ice worms (<i>Mesenchytraeus solifugus</i>) are the largest animals that spend their entire life cycle in ice. Indeed, they spend most of their lives near 0°C and accumulate adenosine trisphosphate (ATP) to mitigate the impacts of cold on their performance. However, the degree to which ice worms can survive temperatures above and below freezing has not been thoroughly investigated. Furthermore, the role of cold temperatures in shaping whole-body metabolism in ice worms is largely unknown. Here, we assessed thermal limits of ice worms as well as their whole-body metabolic rates. Notably, we found that though ice worms can survive short-term exposures to surprisingly warm temperatures (~26°C), they cannot tolerate freezing, including internal ice formation. Ice worm metabolic rates also significantly increased with temperature up to 16°C where a significant break point occurred. Taken together, our results further illuminate how ice worms survive their unique life in ice and highlight how close they live to their lower thermal limits. Looking ahead, we note the clear risks that anthropogenic climate change, glacier recession, and loss of mountain snowpack pose to the future of ice worms.
DOI: 10.6084/m9.figshare.28945691
title: Land cover change across the major proglacial regions of the sub-Antarctic islands, Antarctic Peninsula, and McMurdo Dry Valleys, during the 21<sup>st</sup> century

description: Land cover information is essential for understanding Earth surface processes and ecosystems. Here, we use <i>K</i>-means clustering to classify Landsat 8 Operational Land Imager (OLI) images covering six proglacial sites of sub-Antarctic islands, the Antarctic Peninsula, and the McMurdo Dry Valleys at 30-m resolution. We quantify spatial patterns of water, bedrock, vegetation, and sediments to an accuracy of 77 percent. Vegetation is most abundant on South Georgia (7 percent of the proglacial area) and the South Shetland Islands (1 to 2 percent). Furthermore, we use change vector analysis (CVA) to discriminate landcover change in the twenty-first century. A latitudinal pattern is evident in ice loss and proglacial landscape change; for example, loss of ice on South Georgia and proglacial landcover change is two orders of magnitude greater than in the McMurdo Dry Valleys. Four of the studied sites had similar landscape stability (64 to 68 percent unchanged), with Alexander Island an exception (50 percent change) due to recent enhanced glacier melt. Overall, we show how landcover of proglacial regions of the climaticallysensitive sub-Antarctic and Antarctica has changed since 2000, with a CVA accuracy of 80 percent. These findings inform understanding of geomorphological activity and sediment and nutrient fluxes and hence terrestrial and marine ecosystems.
DOI: 10.6084/m9.figshare.28945691.v1
title: Land cover change across the major proglacial regions of the sub-Antarctic islands, Antarctic Peninsula, and McMurdo Dry Valleys, during the 21<sup>st</sup> century

description: Land cover information is essential for understanding Earth surface processes and ecosystems. Here, we use <i>K</i>-means clustering to classify Landsat 8 Operational Land Imager (OLI) images covering six proglacial sites of sub-Antarctic islands, the Antarctic Peninsula, and the McMurdo Dry Valleys at 30-m resolution. We quantify spatial patterns of water, bedrock, vegetation, and sediments to an accuracy of 77 percent. Vegetation is most abundant on South Georgia (7 percent of the proglacial area) and the South Shetland Islands (1 to 2 percent). Furthermore, we use change vector analysis (CVA) to discriminate landcover change in the twenty-first century. A latitudinal pattern is evident in ice loss and proglacial landscape change; for example, loss of ice on South Georgia and proglacial landcover change is two orders of magnitude greater than in the McMurdo Dry Valleys. Four of the studied sites had similar landscape stability (64 to 68 percent unchanged), with Alexander Island an exception (50 percent change) due to recent enhanced glacier melt. Overall, we show how landcover of proglacial regions of the climaticallysensitive sub-Antarctic and Antarctica has changed since 2000, with a CVA accuracy of 80 percent. These findings inform understanding of geomorphological activity and sediment and nutrient fluxes and hence terrestrial and marine ecosystems.
DOI: 10.6084/m9.figshare.31113344
title: Beringian flora and fauna from <i>sed</i>aDNA at Duvanny Yar, Sakha Republic, during marine isotope stages 2 and 3

description: Beringia’s extensive late Quaternary deposits of yedoma silt provide rich floristic and faunal records. Sampling of frozen yedoma exposures typically uses multiple baydzherakhs (residual silt heaps developed as conical thermokarst mounds) that collectively straddle vertical and horizontal space in thaw slumps. We used radiocarbon-dated vertical and horizontal transects to assess stratigraphic and sampling consistency at Duvanny Yar (a critical late Pleistocene stratotype). Most samples are ascribed to later marine isotope stage (MIS) 3 or to MIS 2 (ca. 40,000 to 20,000 years ago). The horizontal (MIS 3) transect revealed an undulating paleolandscape. Sedimentary ancient DNA (<i>sed</i>aDNA) from frozen yedoma revealed floristic composition in time and space. Metabarcoding and shotgun metagenomic molecular floras and pollen spectra are ecologically consistent. Floristically, little separates MIS 3 and 2; xeric, treeless vegetation, dominated by grasses and forbs including disturbance indicators, characterizes both stages. Holocene samples have a distinct, woody-dominated flora. Ordination of metabarcoding samples from the horizontal transect shows samples are highly variable but do not form a discernible spatial gradient or mosaic, possibly reflecting microtopographically driven plant distribution across a largely homogeneous landscape. Mammals identified from MIS 3 and 2 include horse, steppe bison, woolly rhinoceros, woolly mammoth, reindeer, hare, and vole.
DOI: 10.6084/m9.figshare.31113344.v1
title: Beringian flora and fauna from <i>sed</i>aDNA at Duvanny Yar, Sakha Republic, during marine isotope stages 2 and 3

description: Beringia’s extensive late Quaternary deposits of yedoma silt provide rich floristic and faunal records. Sampling of frozen yedoma exposures typically uses multiple baydzherakhs (residual silt heaps developed as conical thermokarst mounds) that collectively straddle vertical and horizontal space in thaw slumps. We used radiocarbon-dated vertical and horizontal transects to assess stratigraphic and sampling consistency at Duvanny Yar (a critical late Pleistocene stratotype). Most samples are ascribed to later marine isotope stage (MIS) 3 or to MIS 2 (ca. 40,000 to 20,000 years ago). The horizontal (MIS 3) transect revealed an undulating paleolandscape. Sedimentary ancient DNA (<i>sed</i>aDNA) from frozen yedoma revealed floristic composition in time and space. Metabarcoding and shotgun metagenomic molecular floras and pollen spectra are ecologically consistent. Floristically, little separates MIS 3 and 2; xeric, treeless vegetation, dominated by grasses and forbs including disturbance indicators, characterizes both stages. Holocene samples have a distinct, woody-dominated flora. Ordination of metabarcoding samples from the horizontal transect shows samples are highly variable but do not form a discernible spatial gradient or mosaic, possibly reflecting microtopographically driven plant distribution across a largely homogeneous landscape. Mammals identified from MIS 3 and 2 include horse, steppe bison, woolly rhinoceros, woolly mammoth, reindeer, hare, and vole.
DOI: 10.5281/zenodo.14055047
title: Evaluation of the metamodel

description: The questionnaire used is provided in the original paper: Damaševičius, R., & Maskeliūnas, R. (2025). Enhancing Smart Forestry Through AI-Driven Knowledge Management: A Synergistic Approach to Efficiency and Sustainability. Journal of Sustainable Forestry, 44(1–5), 1–31. https://doi.org/10.1080/10549811.2025.2513220

All values were translated from Lithuanian language and free form expressions were standardized to ensure consistent semantic interpretation.

 

Variables

Role:
Years of Experience:
Organization Type:
Used KM Metamodel:
Ease of Use:
Relevance to Forestry Tasks:
Knowledge Retrieval Efficiency:
Decision Support Effectiveness:
Overall Satisfaction:
Specific Benefits:
Knowledge Base Improved Decision-Making:
Specific Instance of Decision-Making:
Suggestions for Improvement:
Additional Features Desired:
Enhanced Knowledge Sharing:
Improved Data Management:
Contributed to Sustainable Forestry:
Would Recommend to Others:
Additional Comments:
DOI: 10.5281/zenodo.14055048
title: Evaluation of the metamodel

description: The questionnaire used is provided in the original paper: Damaševičius, R., & Maskeliūnas, R. (2025). Enhancing Smart Forestry Through AI-Driven Knowledge Management: A Synergistic Approach to Efficiency and Sustainability. Journal of Sustainable Forestry, 44(1–5), 1–31. https://doi.org/10.1080/10549811.2025.2513220

All values were translated from Lithuanian language and free form expressions were standardized to ensure consistent semantic interpretation.

 

Variables

Role:
Years of Experience:
Organization Type:
Used KM Metamodel:
Ease of Use:
Relevance to Forestry Tasks:
Knowledge Retrieval Efficiency:
Decision Support Effectiveness:
Overall Satisfaction:
Specific Benefits:
Knowledge Base Improved Decision-Making:
Specific Instance of Decision-Making:
Suggestions for Improvement:
Additional Features Desired:
Enhanced Knowledge Sharing:
Improved Data Management:
Contributed to Sustainable Forestry:
Would Recommend to Others:
Additional Comments:
DOI: 10.6084/m9.figshare.31113353
title: Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints

description: We study contextual dynamic pricing problems where a firm sells products to <i>T</i> sequentially-arriving consumers, behaving according to an unknown demand model. The firm aims to minimize its regret over a clairvoyant that knows the model in advance. The demand follows a generalized linear model (GLM), allowing for stochastic feature vectors in Rd encoding product and consumer information. We first show the optimal regret is of order dT, up to logarithmic factors, improving existing upper bounds by a d factor, achieved by an explore-then-commit (ETC) algorithm. We further study contextual dynamic pricing under local differential privacy (LDP) constraints. We propose a stochastic gradient descent-based ETC algorithm achieving regret upper bounds of order dT/ϵ, up to logarithmic factors, where is the privacy parameter. The upper bounds with and without LDP constraints are matched by newly constructed minimax lower bounds, characterizing costs of privacy. Moreover, we extend our study to dynamic pricing under mixed privacy constraints, which naturally bridges private and non-private dynamic pricing. We propose a two-stage ETC algorithm and show that it improves the privacy-utility tradeoff by efficiently leveraging public data. Its optimality is further established via a newly-derived minimax lower bound. To our knowledge, this is the first time such setting is studied in the dynamic pricing literature. Extensive numerical experiments and real data applications are conducted to illustrate the efficiency and practical value of our algorithms.
DOI: 10.6084/m9.figshare.31113353.v1
title: Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints

description: We study contextual dynamic pricing problems where a firm sells products to <i>T</i> sequentially-arriving consumers, behaving according to an unknown demand model. The firm aims to minimize its regret over a clairvoyant that knows the model in advance. The demand follows a generalized linear model (GLM), allowing for stochastic feature vectors in Rd encoding product and consumer information. We first show the optimal regret is of order dT, up to logarithmic factors, improving existing upper bounds by a d factor, achieved by an explore-then-commit (ETC) algorithm. We further study contextual dynamic pricing under local differential privacy (LDP) constraints. We propose a stochastic gradient descent-based ETC algorithm achieving regret upper bounds of order dT/ϵ, up to logarithmic factors, where is the privacy parameter. The upper bounds with and without LDP constraints are matched by newly constructed minimax lower bounds, characterizing costs of privacy. Moreover, we extend our study to dynamic pricing under mixed privacy constraints, which naturally bridges private and non-private dynamic pricing. We propose a two-stage ETC algorithm and show that it improves the privacy-utility tradeoff by efficiently leveraging public data. Its optimality is further established via a newly-derived minimax lower bound. To our knowledge, this is the first time such setting is studied in the dynamic pricing literature. Extensive numerical experiments and real data applications are conducted to illustrate the efficiency and practical value of our algorithms.
DOI: 10.6084/m9.figshare.30234951
title: Binary Outcomes and Linear Interactions

description: We consider general models of linear interactions in binary outcomes. We first characterize the conditions under which these models are coherent and complete. Next, we show that models of linear interactions admit sound microfoundations under assumptions of incomplete information and independence. To help guide practitioners, we discuss key modeling choices and testable restrictions. We then consider a specification that is linear in parameters, and propose a simple Two-Stage Least Squares estimator. We revisit the empirical analysis of teenage smoking and peer effects of <i>Lee, Li, and Lin</i>. Our reanalysis highlights the main advantages of the linear framework, including minimal computational and data requirements and ability to handle fixed effects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30234951.v2
title: Binary Outcomes and Linear Interactions

description: We consider general models of linear interactions in binary outcomes. We first characterize the conditions under which these models are coherent and complete. Next, we show that models of linear interactions admit sound microfoundations under assumptions of incomplete information and independence. To help guide practitioners, we discuss key modeling choices and testable restrictions. We then consider a specification that is linear in parameters, and propose a simple Two-Stage Least Squares estimator. We revisit the empirical analysis of teenage smoking and peer effects of <i>Lee, Li, and Lin</i>. Our reanalysis highlights the main advantages of the linear framework, including minimal computational and data requirements and ability to handle fixed effects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30234951.v1
title: Binary Outcomes and Linear Interactions<sup>†</sup>

description: We consider general models of linear interactions in binary outcomes. We first characterize the conditions under which these models are coherent and complete. Next, we show that models of linear interactions admit sound microfoundations under assumptions of incomplete information and independence. To help guide practitioners, we discuss key modeling choices and testable restrictions. We then consider a specification that is linear in parameters, and propose a simple Two-Stage Least Squares estimator. We revisit the empirical analysis of teenage smoking and peer effects of Lee, Li, and Lin (2014). Our reanalysis highlights the main advantages of the linear framework, including minimal computational and data requirements and ability to handle fixed effects.
DOI: 10.6084/m9.figshare.30458160.v1
title: Cross-Section Bootstrap for CCE Regressions with General Unknown Factors

description: The Common Correlated Effects (CCE) approach enjoys considerable popularity for estimating factor-augmented panel data models. A key benefit is that by orthogonalizing the data on the available cross-section averages, the unobserved components are eliminated from the data, regardless of their order(s) of integration. This obviates the need for such knowledge, and makes CCE particularly attractive for macroeconomic applications, where the set of unobservables might contain both stationary and non-stationary variables. Despite of these benefits, it is often neglected that the pooled CCE (CCEP) estimator suffers from an asymptotic bias in TN−1→τ&gt;0 panels, which too is common in macroeconomic research. This bias is highly disruptive for inference but cannot generally be remedied with analytical corrections. As such, we establish in this paper the validity of the cross-section bootstrap under general unknown factors for TN−1→τ&lt;∞ as (N,T)→∞. We show that the scheme replicates the distribution of the CCE estimators, leading to a straightforward bias-correction, and confidence intervals that enable asymptotically valid inference for τ&lt;∞. This significantly broadens the applicability of the CCEP method. We in addition explore the case where idiosyncratics serve as another source of non-stationarity. Simulation experiments show that our theoretical predictions translate well to finite samples, and that the methodology outperforms alternative bias-corrections and estimators. The method is finally demonstrated with a gravity trade application based on the dataset of Serlenga and Shin (2007).
DOI: 10.6084/m9.figshare.31066845
title: Deep Orthogonal Learner for Conditional Quantile Treatment Effect Estimation

description: In recent years, orthogonal statistical learning has been widely recognized for its ability to reduce sensitivity with respect to nuisance parameters to estimate the target parameter, making it an important tool in causal inference, particularly in the estimation of the conditional average treatment effect (CATE). However, its application on conditional quantile treatment effect (CQTE), which offers a more comprehensive perspective on treatment effects than CATE, has not yet been explored comprehensively. In this article, we propose a novel method for learning CQTE. This method shares Neyman-orthogonal property, which produces CQTE estimators that are insensitive to small perturbations of nuisance functions. We first model the CQTE nonparametrically and use deep learning to estimate it. We establish the convergence rate of the neural network estimator, demonstrating that it achieves the minimax optimal rate of convergence (up to a polylogarithmic factor). This highlights deep learning’s ability to identify low-dimensional structures in high-dimensional data. Additionally, we then model CQTE linearly to facilitate interpretation and statistical inference. We prove that the corresponding coefficient and CQTE estimators achieve root-<i>n</i> consistency and asymptotic normality, even if the estimators of the nuisance parameters converge at a slower rate. Through empirical evaluation for numerical studies, we demonstrate the superiority of our method compared to competing methods.
DOI: 10.6084/m9.figshare.31066845.v1
title: Deep Orthogonal Learner for Conditional Quantile Treatment Effect Estimation

description: In recent years, orthogonal statistical learning has been widely recognized for its ability to reduce sensitivity with respect to nuisance parameters to estimate the target parameter, making it an important tool in causal inference, particularly in the estimation of the conditional average treatment effect (CATE). However, its application on conditional quantile treatment effect (CQTE), which offers a more comprehensive perspective on treatment effects than CATE, has not yet been explored comprehensively. In this article, we propose a novel method for learning CQTE. This method shares Neyman-orthogonal property, which produces CQTE estimators that are insensitive to small perturbations of nuisance functions. We first model the CQTE nonparametrically and use deep learning to estimate it. We establish the convergence rate of the neural network estimator, demonstrating that it achieves the minimax optimal rate of convergence (up to a polylogarithmic factor). This highlights deep learning’s ability to identify low-dimensional structures in high-dimensional data. Additionally, we then model CQTE linearly to facilitate interpretation and statistical inference. We prove that the corresponding coefficient and CQTE estimators achieve root-<i>n</i> consistency and asymptotic normality, even if the estimators of the nuisance parameters converge at a slower rate. Through empirical evaluation for numerical studies, we demonstrate the superiority of our method compared to competing methods.
DOI: 10.6084/m9.figshare.30492857
title: Supervised Predictive Modeling of High-Dimensional Data with Group ℓ<sup>0</sup>-Norm Constrained Neural Networks

description: To flexibly handle high-dimensional data, this paper develops a new sparsity-constrained optimization approach. By leveraging group ℓ0-norm constrained neural networks, the proposed approach aims to simultaneously extract crucial features and estimate the underlying model function with statistically guaranteed accuracy. Under some mild conditions, we establish statistical theories of the proposed method for a general class of nonparametric regression-type loss functions. Moreover, two iterative greedy selection algorithms, which iterate between a standard gradient descent step and a hard thresholding step with or without debiasing, are presented to implement the computation. Convergence guarantees and sparsity recovery capabilities of these algorithms are rigorously examined. Empirical validation through a series of comprehensive experiments conducted on both real-world and synthetic datasets underscores the superior performance of our proposed estimator. In various scenarios, the proposed approach surpasses conventional Lasso-based sparse learning methods in terms of variable selection accuracy and prediction performance, thereby highlighting its efficacy in practical applications.