cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31149217
title: Complementary data for "<b>The birth, death, and evolutionary compensation of uORFs in </b><b><i>Drosophila</i></b>"

description: Upstream open reading frames (uORFs) are critical regulators of mRNA translation, yet their evolutionary dynamics remain poorly understood. Here, we analyze uORF evolution across <i>Drosophila</i> species and uncover pervasive birth–death turnover. This process is characterized by a persistent excess of upstream start codon (uATG) gains over losses, shaped by the interplay of mutational input and natural selection. We find that the evolutionary conservation of uATGs is strongly associated with translational evidence, indicating a tight coupling between uORF retention and translational output. Lineage-specific uATGs are linked to reduced translation of downstream coding sequences, revealing lineage-dependent regulatory effects. We further identify evolutionary compensation between uATG gain and loss events within genes, supported by functional assays demonstrating frequent and condition-dependent effects on translation. At the population level, canonical uORF variants show signatures of population-specific selection, suggesting a role for uORF turnover in local adaptation. Together, our results reveal how natural selection, translational regulation, and evolutionary turnover jointly shape the uORF landscape in <i>Drosophila</i>.
DOI: 10.6084/m9.figshare.31149217.v1
title: Complementary data for "<b>The birth, death, and evolutionary compensation of uORFs in </b><b><i>Drosophila</i></b>"

description: Upstream open reading frames (uORFs) are critical regulators of mRNA translation, yet their evolutionary dynamics remain poorly understood. Here, we analyze uORF evolution across <i>Drosophila</i> species and uncover pervasive birth–death turnover. This process is characterized by a persistent excess of upstream start codon (uATG) gains over losses, shaped by the interplay of mutational input and natural selection. We find that the evolutionary conservation of uATGs is strongly associated with translational evidence, indicating a tight coupling between uORF retention and translational output. Lineage-specific uATGs are linked to reduced translation of downstream coding sequences, revealing lineage-dependent regulatory effects. We further identify evolutionary compensation between uATG gain and loss events within genes, supported by functional assays demonstrating frequent and condition-dependent effects on translation. At the population level, canonical uORF variants show signatures of population-specific selection, suggesting a role for uORF turnover in local adaptation. Together, our results reveal how natural selection, translational regulation, and evolutionary turnover jointly shape the uORF landscape in <i>Drosophila</i>.
DOI: 10.5517/ccdc.csd.cc2k1g9d
title: CCDC 2354893: Experimental Crystal Structure Determination

description: Related Article: Akilesh Manimuthu, Pavithra Balakrishnan, Nandhini Mariyappan, Nattamai S.P. Bhuvanesh, Thirunavukkarasu Velusamy, Vijayakumar Subramaniam, Dharmaraj Nallasamy|2026|J.Mol.Struct.|1357||doi:10.1016/j.molstruc.2026.145257
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.cc2k416z
title: CCDC 2357370: Experimental Crystal Structure Determination

description: Related Article: Florian Fricke, Gerald Dräger, Andreas Kirschning|2025|React. Chem. Eng.|||doi:10.1039/D4RE00394B
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.cc2l81mk
title: CCDC 2391018: Experimental Crystal Structure Determination

description: Related Article: Akilesh Manimuthu, Pavithra Balakrishnan, Nandhini Mariyappan, Nattamai S.P. Bhuvanesh, Thirunavukkarasu Velusamy, Vijayakumar Subramaniam, Dharmaraj Nallasamy|2026|J.Mol.Struct.|1357||doi:10.1016/j.molstruc.2026.145257
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.cc2l82nm
title: CCDC 2391050: Experimental Crystal Structure Determination

description: Related Article: Václav Ráliš, Kamila Dostálová, Jiří Kubišta, Róbert Gyepes, Vojtěch Hamala, Roman Hrstka, Jiří Pinkas|2026|Polyhedron|287||doi:10.1016/j.poly.2025.117954
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.cc2lcgdv
title: CCDC 2394297: Experimental Crystal Structure Determination

description: Related Article: Jie Li, Zhang-Li Cheng, Shu-Qi Zhang, Yue Wang, Hui Wang, Jie-Yu Zhou, Wen Gao, Ling-Wei Qu, Zhe-Yu Zhu, Jia Yu, Jun Ye, Kai Wang|2025|Phys.Chem.Chem.Phys.(PCCP)|28|2049|doi:10.1039/d5cp04299b
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.cc2lpc9z
title: CCDC 2403811: Experimental Crystal Structure Determination

description: Related Article: Hieu Minh Ngo, Yunseung Kuk, Jihyun Lee, Jiachen Lu, Zhiyong Bai, Chun-Li Hu, Kang Min Ok|2025|Adv. Opt. Mater.|13|checked|doi:10.1002/adom.202403241
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.cc2l5w8y
title: CCDC 2388930: Experimental Crystal Structure Determination

description: Related Article: Yi Xie, Jack Morgenstein, Kameron R. Hansen, Heshan Hewa-Walpitage, Carter M. Shirley, Purusharth Amrut, Daniel Nikiforov, Kathryn Bairley, Junxiang Zhang, Naidel A. M. S. Caturello, Sasa Wang, Trigg Randall, Levi Homer, Garrett Davis, Stephen Barlow, Seth R. Marder, Zeev Valy Vardeny, John S. Colton, Volker Blum, and David B. Mitzi|2025|ACS Nano|19|12895|doi:10.1021/acsnano.4c16359
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.cc2lvhsr
title: CCDC 2408755: Experimental Crystal Structure Determination

description: Related Article: Tomasz Uchacz, Paweł Szlachcic, Marcin Andrzejak, Joanna Zams, Piotr Wieczorek, Przemysław Kolek, Katarzyna Stadnicka, Andrzej Danel, Arkadiusz Gut|2026|J.Lumin.|292||doi:10.1016/j.jlumin.2026.121747
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.cc2lvhvt
title: CCDC 2408757: Experimental Crystal Structure Determination

description: Related Article: Tomasz Uchacz, Paweł Szlachcic, Marcin Andrzejak, Joanna Zams, Piotr Wieczorek, Przemysław Kolek, Katarzyna Stadnicka, Andrzej Danel, Arkadiusz Gut|2026|J.Lumin.|292||doi:10.1016/j.jlumin.2026.121747
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: 10.5281/zenodo.18323822
title: Integrated Hydrological Model of Skjern Å catchment: MIKE SHE model setup, inputs, outputs and observation data

description: MIKE SHE is an integrated hydrological modeling system that emerged in the 1980s to represent the physical processes of water flow through the hydrological cycle in a modular way and with relevant scale. The available process modules - evapotranspiration, overland flow, unsaturated zone flow, groundwater flow, channel flow - are each governed by their relevant partial differential equations describing the movement of water through their system. This repository contains an example model setup, inputs, outputs and observation data for a MIKE SHE Integrated Hydrological Model of the Skjern River catchment located on the west coast of Jutland, Denmark. This model is a subset of the DK-model developed by GEUS (see DK-model).

 

This dataset is part of the WaterBench series by DHI, supporting open research on water-related challenges. It is intended for educational and research purposes only, such as model validation, parameter calibration, and machine learning applications. The model resolution has been reduced from 100m to 500m grid cells compared to the DK-model and is uncalibrated. The performance shown here does not reflect that of DHI's high-resolution models. Results must not be used for decision-making.



Files:





README: Description of dataset with details on citations, data processing, and background information.

WaterBench-MIKESHE-SKjern.zip: model setup, input data, observational data, and code for data exploration and model validation.

Waterbench-MIKESHE-SKjern-output.zip: result files (Actual evapotranspiration, head elevation in saturated zone, groundwater flow, etc.) for 20 years of simulation.


Version 2 with updated README.pdf
DOI: 10.5281/zenodo.18376532
title: Integrated Hydrological Model of Skjern Å catchment: MIKE SHE model setup, inputs, outputs and observation data

description: MIKE SHE is an integrated hydrological modeling system that emerged in the 1980s to represent the physical processes of water flow through the hydrological cycle in a modular way and with relevant scale. The available process modules - evapotranspiration, overland flow, unsaturated zone flow, groundwater flow, channel flow - are each governed by their relevant partial differential equations describing the movement of water through their system. This repository contains an example model setup, inputs, outputs and observation data for a MIKE SHE Integrated Hydrological Model of the Skjern River catchment located on the west coast of Jutland, Denmark. This model is a subset of the DK-model developed by GEUS (see DK-model).

 

This dataset is part of the WaterBench series by DHI, supporting open research on water-related challenges. It is intended for educational and research purposes only, such as model validation, parameter calibration, and machine learning applications. The model resolution has been reduced from 100m to 500m grid cells compared to the DK-model and is uncalibrated. The performance shown here does not reflect that of DHI's high-resolution models. Results must not be used for decision-making.



Files:





README: Description of dataset with details on citations, data processing, and background information.

WaterBench-MIKESHE-SKjern.zip: model setup, input data, observational data, and code for data exploration and model validation.

Waterbench-MIKESHE-SKjern-output.zip: result files (Actual evapotranspiration, head elevation in saturated zone, groundwater flow, etc.) for 20 years of simulation.


Version 2 with updated README.pdf
DOI: 10.26275/kc21-shtp
title: Sacral nerve stimulation mapping with conscious ambulatory feline bladder monitoring

description: A FitBit for the Bladder
description: Mapping bladder function in ambulatory cats in response to sacral root stimulation, using wireless UroMOCA devices and wireless StimPod stimulators.
DOI: 10.5281/zenodo.17490530
title: A lake salinity dataset produced via microwave and optical imageries

description: Lake salinity is an important parameter to characterize physical and biogeochemical processes and a fundamental indicator to evaluate lake water quality. However, its estimation in inland waters has been challenging because passive microwave salinity satellites lack sufficient spatial resolution, and optical satellites cannot directly measure it. Here, constructed a framework for estimating lake salinity by combining Synthetic Aperture Radar (SAR) and Multi-Spectral Instrument (MSI) data. On this basis, we reconstructed the lake salinity dataset for 2016–2024 in the Inner Mongolia Xinjiang Lake zone (IMXL) and revealed their spatial pattern.
DOI: 10.5281/zenodo.18371515
title: A lake salinity dataset produced via microwave and optical imageries

description: Lake salinity is an important parameter to characterize physical and biogeochemical processes and a fundamental indicator to evaluate lake water quality. However, its estimation in inland waters has been challenging because passive microwave salinity satellites lack sufficient spatial resolution, and optical satellites cannot directly measure it. Here, constructed a framework for estimating lake salinity by combining Synthetic Aperture Radar (SAR) and Multi-Spectral Instrument (MSI) data. On this basis, we reconstructed the lake salinity dataset for 2016–2024 in the Inner Mongolia Xinjiang Lake zone (IMXL) and revealed their spatial pattern.
DOI: 10.6084/m9.figshare.28334671
title: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

description: Understanding the oscillating behaviors that govern organisms’ internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, <i>Neurospora crassa.</i>
DOI: 10.6084/m9.figshare.28334671.v1
title: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

description: Understanding the oscillating behaviors that govern organisms’ internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, <i>Neurospora crassa.</i>
DOI: 10.15159/phytop.155
title: Endla

description: Phytoplankton biomass, diversity and abundance in samples collected from Lake Endla during the period 1951-2008.
DOI: 10.26008/1912/bco-dmo.986889.1
title: Integrative taxonomy of introduced Haplosclerida and three new species of Haliclona sponges from Hawai'i based on samples collected from a variety of habitats on O'ahu from 2016 to 2022

description: This data supports the description of the following haplosclerida species in Hawai'i: Haliclona (Soestella) caerulea and Gelliodes conulosa sp. nov.,  Haliclona (Gellius) pahua sp. nov., Haliclona (Reniera) kahoe sp. nov., Haliclona (Rhizoniera) loe sp. nov. Accession numbers to mitochondrial and ribosomal RNA sequences are provided. Morphological data for each of the species are also provided.
DOI: 10.6084/m9.figshare.28525364
title: Noise Resistant Control Charts for Detecting Periodicity from Correlation

description: Noise Resistant Control Charts for Detecting Periodicity from Correlation
DOI: 10.6084/m9.figshare.28525364.v1
title: Noise Resistant Control Charts for Detecting Periodicity from Correlation

description: Noise Resistant Control Charts for Detecting Periodicity from Correlation
DOI: 10.6084/m9.figshare.28599380
title: Assessment of Case Influence in the Lasso with a Case-Weight Adjusted Solution Path

description: We study case influence in the Lasso regression using Cook’s distance which measures overall change in the fitted values when one observation is deleted. Unlike in ordinary least squares regression, the estimated coefficients in the Lasso do not have a closed form due to the nondifferentiability of the l1 penalty, and neither does Cook’s distance. To find the case-deleted Lasso solution without refitting the model, we approach it from the full data solution by introducing a weight parameter ranging from 1 to 0 and generating a solution path indexed by this parameter. We show that the solution path is piecewise linear with respect to a simple function of the weight parameter under a fixed penalty. The resulting case influence is a function of the penalty and weight, and it becomes Cook’s distance when the weight is 0. As the penalty parameter changes, selected variables change, and the magnitude of Cook’s distance for the same data point may vary with the subset of variables selected. In addition, we introduce a case influence graph to visualize how the contribution of each data point changes with the penalty parameter. From the graph, we can identify influential points at different penalty levels and make modeling decisions accordingly. Moreover, we find that case influence graphs exhibit different patterns between underfitting and overfitting phases, which can provide additional information for model selection.
DOI: 10.6084/m9.figshare.28599380.v3
title: Assessment of Case Influence in the Lasso with a Case-Weight Adjusted Solution Path

description: We study case influence in the Lasso regression using Cook’s distance which measures overall change in the fitted values when one observation is deleted. Unlike in ordinary least squares regression, the estimated coefficients in the Lasso do not have a closed form due to the nondifferentiability of the l1 penalty, and neither does Cook’s distance. To find the case-deleted Lasso solution without refitting the model, we approach it from the full data solution by introducing a weight parameter ranging from 1 to 0 and generating a solution path indexed by this parameter. We show that the solution path is piecewise linear with respect to a simple function of the weight parameter under a fixed penalty. The resulting case influence is a function of the penalty and weight, and it becomes Cook’s distance when the weight is 0. As the penalty parameter changes, selected variables change, and the magnitude of Cook’s distance for the same data point may vary with the subset of variables selected. In addition, we introduce a case influence graph to visualize how the contribution of each data point changes with the penalty parameter. From the graph, we can identify influential points at different penalty levels and make modeling decisions accordingly. Moreover, we find that case influence graphs exhibit different patterns between underfitting and overfitting phases, which can provide additional information for model selection.
DOI: 10.6084/m9.figshare.27241919
title: Experimental Design and Modeling for Forward-Inverse Maps

description: In customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation <i>metamodel</i> of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical.
DOI: 10.6084/m9.figshare.27241919.v1
title: Experimental Design and Modeling for Forward-Inverse Maps

description: In customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation <i>metamodel</i> of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical.