cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5838999
title: Sensor data for "Influence of X-Ray Radiation on Historical Paper"

description: This is the raw and processed data used in the paper "Influence of X-Ray Radiation on Historical Paper". It consists of the following: all images in the CR3 format, taken before, during, and after irradiation of the paper, the images converted to JPEG, as well as crops of the relevant parts stored as nupy arrays
DOI: 10.6073/pasta/3706b23cf7ee38d65cd2adc3d8621c8d
title: Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.

description: The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
DOI: 10.21233/skg8-w860
title: Puerto Edén insect dataset

description: Raw data for the Puerto Edén insect dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/c6ch-s503
title: Lemelerveld 2 Raalte pollen dataset

description: Raw data for the Lemelerveld 2 Raalte pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/17y5-9k61
title: Oosterwolde F1 pollen dataset

description: Raw data for the Oosterwolde F1 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/sbjr-0f96
title: Rande pollen dataset

description: Raw data for the Rande pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/c0q8-6c86
title: Koordes pollen dataset

description: Raw data for the Koordes pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/vrcf-2c49
title: Oosterwolde F1 pollen dataset

description: Raw data for the Oosterwolde F1 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/j8p9-g487
title: Sulsseewli charcoal dataset

description: Raw data for the Sulsseewli charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/nm0y-gm04
title: Sulsseewli pollen dataset

description: Raw data for the Sulsseewli pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/zr6w-6h57
title: Puerto Varas insect dataset

description: Raw data for the Puerto Varas insect dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.21082651
title: Efficient posterior sampling for Bayesian Poisson regression

description: Poisson log-linear models are ubiquitous in many applications, and one of the most popular approaches for parametric count regression. In the Bayesian context, however, there are no sufficient specific computational tools for efficient sampling from the posterior distribution of parameters, and standard algorithms, such as random walk Metropolis-Hastings or Hamiltonian Monte Carlo algorithms, are typically used. Herein, we developed an efficient Metropolis-Hastings algorithm and importance sampler to simulate from the posterior distribution of the parameters of Poisson log-linear models under conditional Gaussian priors with superior performance with respect to the state-of-the-art alternatives. The key for both algorithms is the introduction of a proposal density based on a Gaussian approximation of the posterior distribution of parameters. Specifically, our result leverages the negative binomial approximation of the Poisson likelihood and the successful Pólya-gamma data augmentation scheme. Via simulation, we obtained that the time per independent sample of the proposed samplers is competitive with that obtained using the successful Hamiltonian Monte Carlo sampling, with the Metropolis-Hastings showing superior performance in all scenarios considered.
DOI: 10.6084/m9.figshare.21082651.v1
title: Efficient posterior sampling for Bayesian Poisson regression

description: Poisson log-linear models are ubiquitous in many applications, and one of the most popular approaches for parametric count regression. In the Bayesian context, however, there are no sufficient specific computational tools for efficient sampling from the posterior distribution of parameters, and standard algorithms, such as random walk Metropolis-Hastings or Hamiltonian Monte Carlo algorithms, are typically used. Herein, we developed an efficient Metropolis-Hastings algorithm and importance sampler to simulate from the posterior distribution of the parameters of Poisson log-linear models under conditional Gaussian priors with superior performance with respect to the state-of-the-art alternatives. The key for both algorithms is the introduction of a proposal density based on a Gaussian approximation of the posterior distribution of parameters. Specifically, our result leverages the negative binomial approximation of the Poisson likelihood and the successful Pólya-gamma data augmentation scheme. Via simulation, we obtained that the time per independent sample of the proposed samplers is competitive with that obtained using the successful Hamiltonian Monte Carlo sampling, with the Metropolis-Hastings showing superior performance in all scenarios considered.
DOI: 10.5281/zenodo.7072375
title: Dataset for paper [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique]

description: Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.
DOI: 10.5281/zenodo.7072374
title: Dataset for paper [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique]

description: Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.
DOI: 10.5281/zenodo.7072406
title: IODP Expedition 351 Carbonates composite report

description: This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
DOI: 10.5281/zenodo.7072405
title: IODP Expedition 351 Carbonates composite report

description: This composite report includes data from two analyses (total carbon from Elemental analysis [CHNS], and inorganic carbon from [Coulometer]). Each row combines the CHNS and Coulometer data from measurements made on the same sample at the same time for a particular section and section offset (depth). If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate data type (Elemental analysis and Coulometer). If the same sample was measured multiple times by any of the methods, results in the report will be combined on one line where possible. Each additional replicate result will be shown in subsequent rows and will be combined where possible. Report includes results for carbon forms: total, inorganic, calcium carbonate, and organic by difference, along with total hydrogen, nitrogen, and sulfur.
DOI: 10.5281/zenodo.7072411
title: IODP Expedition 351 Elemental analysis (CHNS)

description: Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
DOI: 10.5281/zenodo.7072410
title: IODP Expedition 351 Elemental analysis (CHNS)

description: Fundamental elemental component (total carbon, hydrogen, nitrogen, and sulfur) fluctuations help define the origin, depositional environment, and diagenetic alteration of source materials. To determine C, H, N, and S, solid samples are reacted with a catalyst, separated by chromatography, and detected by thermal conductivity on a FlashEA 1112 CHNS elemental analyzer. Organic carbon can be directly measured on the elemental analyzer by acidification of the sample to drive off carbonate as carbon dioxide before analyzing. Total organic carbon on this report is measured rather than calculated.
DOI: 10.5281/zenodo.7072459
title: IODP Expedition 351 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.5281/zenodo.7072458
title: IODP Expedition 351 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.5281/zenodo.7072487
title: IODP Expedition 351 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.5281/zenodo.7072486
title: IODP Expedition 351 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.6084/m9.figshare.21082699
title: Supplementary material 1.docx

description: We provide the suplementary table 1 of our article entitled "X-linked hypophosphatemia, not only a skeletal disease but also a chronic inflammatory state". We assessed inflammation among XLH patients in their blood cells and osteoclasts using RT-qPCR techniques. We provide here the primers used for the eight genes tested in our study.
DOI: 10.5281/zenodo.7072480
title: IODP Expedition 351 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.5281/zenodo.7072479
title: IODP Expedition 351 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.5281/zenodo.7072476
title: IODP Expedition 351 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.5281/zenodo.7072475
title: IODP Expedition 351 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.6084/m9.figshare.21083356
title: 117_ideal.pdb

description: Prepared by using openBabel ( OPENBABEL - Chemical file format converter (cheminfo.org) ) on 2022-09-12 as follows: <br> Using 117_ideal.cif (source: https://www.rcsb.org/ligand/117) converted to .pdb format with default settings (Generate Coordinates: None, Add/Delete Hydrogens: No Change, pH to add hydrogens: LEFT BLANK)
DOI: 10.6084/m9.figshare.21083356.v1
title: 117_ideal.pdb

description: Prepared by using openBabel ( OPENBABEL - Chemical file format converter (cheminfo.org) ) on 2022-09-12 as follows: <br> Using 117_ideal.cif (source: https://www.rcsb.org/ligand/117) converted to .pdb format with default settings (Generate Coordinates: None, Add/Delete Hydrogens: No Change, pH to add hydrogens: LEFT BLANK)
DOI: 10.5281/zenodo.7072474
title: IODP Expedition 351 Inorganic carbon (coulometer)

description: Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
DOI: 10.5281/zenodo.7072473
title: IODP Expedition 351 Inorganic carbon (coulometer)

description: Inorganic carbon (carbonate) is determined by coulometry, which uses a photodetection cell to measure carbon dioxide evolved during sample acidification. Report includes percent inorganic carbon and calcium carbonate.
DOI: 10.5517/ccdc.csd.cc2btpd8
title: CCDC 2169640: Experimental Crystal Structure Determination

description: Related Article: Kelsey R. Cairns, Rhys P. King, William Levason, Gillian Reid|2022|CrystEngComm|24|6137|doi:10.1039/D2CE01029A
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.cc2bv8l2
title: CCDC 2170204: Experimental Crystal Structure Determination

description: Related Article: Ruoyao Guo, Liding Wang, Zelun Cai, Zifeng Zhao, Zuqiang Bian, Zhiwei Liu|2022|Inorg.Chem.|61|14164|doi:10.1021/acs.inorgchem.2c02353
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.cc294bh9
title: CCDC 2119330: Experimental Crystal Structure Determination

description: Related Article: Jian-Wei Zhang, Xi Li, Rui-Ying Yu, Jin-Ping Zhang, Ya Chen, Jie-Qiong Li|2022|CrystEngComm|24|6141|doi:10.1039/D2CE01008A
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.cc2bwff3
title: CCDC 2171315: Experimental Crystal Structure Determination

description: Related Article: Wenwen Wei, Xue Zhang, Liping Lu, Sisi Feng|2022|CrystEngComm|24|6204|doi:10.1039/D2CE00626J
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.cc2bwfg4
title: CCDC 2171316: Experimental Crystal Structure Determination

description: Related Article: Wenwen Wei, Xue Zhang, Liping Lu, Sisi Feng|2022|CrystEngComm|24|6204|doi:10.1039/D2CE00626J
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.cc2bwfh5
title: CCDC 2171317: Experimental Crystal Structure Determination

description: Related Article: Wenwen Wei, Xue Zhang, Liping Lu, Sisi Feng|2022|CrystEngComm|24|6204|doi:10.1039/D2CE00626J
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.cc2bwfj6
title: CCDC 2171318: Experimental Crystal Structure Determination

description: Related Article: Wenwen Wei, Xue Zhang, Liping Lu, Sisi Feng|2022|CrystEngComm|24|6204|doi:10.1039/D2CE00626J
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.cc2bwfk7
title: CCDC 2171319: Experimental Crystal Structure Determination

description: Related Article: Wenwen Wei, Xue Zhang, Liping Lu, Sisi Feng|2022|CrystEngComm|24|6204|doi:10.1039/D2CE00626J
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.cc2bz2gw
title: CCDC 2173858: Experimental Crystal Structure Determination

description: Related Article: Duanliang Wang, Defu Sun, Shoubao Zhang, Jiancai Leng, Ruoxian Hou, Hanzhang Chen, Jie Sun, Yangyang Dang, Chuanying Shen|2022|CrystEngComm|24|6232|doi:10.1039/D2CE00731B
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.cc2bz9tg
title: CCDC 2174086: Experimental Crystal Structure Determination

description: Related Article: Kai Shi, Chengrong Lu, Bei Zhao|2022|New J.Chem.|46|16998|doi:10.1039/D2NJ03130B
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.