cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/94kt-pv36
title: Chillingham Wild Cattle Park pollen dataset

description: Raw data for the Chillingham Wild Cattle Park pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/4j7j-dp20
title: Wathumba pollen dataset

description: Raw data for the Wathumba pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/y8dt-7z30
title: Witherspoon Swamp pollen dataset

description: Raw data for the Witherspoon Swamp pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/b74s-v693
title: Witherspoon Swamp pollen dataset

description: Raw data for the Witherspoon Swamp pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080392
title: Additional file 1 of Resource allocation of in vitro fertilization: a nationwide register-based cohort study

description: Additional file 1: Type of IVF cycle, expenditures included in cost calculations in each type of cycle, as well as type and data source of expenditures by care sector. (XLS 16 KB)
DOI: 10.21233/44ns-qq95
title: Triangle Pond pollen dataset

description: Raw data for the Triangle Pond pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/gctj-ed90
title: Zminje Jezero microcharcoal dataset

description: Raw data for the Zminje Jezero microcharcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080412
title: Additional file 2 of Mining a stroke knowledge graph from literature

description: Additional file 2. The list of stroke-related symptoms.
DOI: 10.21233/05xe-xe69
title: Zminje Jezero charcoal dataset

description: Raw data for the Zminje Jezero charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hwh8-9e61
title: CHUS35 pollen surface sample dataset

description: Raw data for the CHUS35 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/s7q1-cr75
title: CHUS36 pollen surface sample dataset

description: Raw data for the CHUS36 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080392.v1
title: Additional file 1 of Resource allocation of in vitro fertilization: a nationwide register-based cohort study

description: Additional file 1: Type of IVF cycle, expenditures included in cost calculations in each type of cycle, as well as type and data source of expenditures by care sector. (XLS 16 KB)
DOI: 10.21233/z3d5-pm84
title: CHUS37 pollen surface sample dataset

description: Raw data for the CHUS37 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ntg0-b243
title: CHUS38 pollen surface sample dataset

description: Raw data for the CHUS38 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/50gx-6956
title: CHUS39 pollen surface sample dataset

description: Raw data for the CHUS39 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.21896199
title: Evaluating Industry 4.0 technology and sustainable development goals – a social perspective

description: Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that ‘Big Data and Analytics’ and ‘Cloud Computing’ are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing ‘Responsible consumption and production’ and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs.
DOI: 10.21233/w3wg-rt48
title: CNTA1 pollen surface sample dataset

description: Raw data for the CNTA1 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.21896199.v1
title: Evaluating Industry 4.0 technology and sustainable development goals – a social perspective

description: Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that ‘Big Data and Analytics’ and ‘Cloud Computing’ are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing ‘Responsible consumption and production’ and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs.
DOI: 10.21233/699c-j472
title: CNTA2 pollen surface sample dataset

description: Raw data for the CNTA2 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/r62f-p453
title: CNTA3 pollen surface sample dataset

description: Raw data for the CNTA3 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ry9n-3281
title: CNTA4 pollen surface sample dataset

description: Raw data for the CNTA4 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/52e7-yt54
title: CNTA5 pollen surface sample dataset

description: Raw data for the CNTA5 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/hr2r-j468
title: CNTA6 pollen surface sample dataset

description: Raw data for the CNTA6 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/b1wc-a985
title: CNTA7 pollen surface sample dataset

description: Raw data for the CNTA7 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/jd3r-mn83
title: CNTA8 pollen surface sample dataset

description: Raw data for the CNTA8 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/e3zr-a543
title: CNTA9 pollen surface sample dataset

description: Raw data for the CNTA9 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080412.v1
title: Additional file 2 of Mining a stroke knowledge graph from literature

description: Additional file 2. The list of stroke-related symptoms.
DOI: 10.6084/m9.figshare.24047050
title: Bayesian Multi-task Variable Selection with an Application to Differential DAG Analysis

description: We study the Bayesian multi-task variable selection problem, where the goal is to select activated variables for multiple related data sets simultaneously. We propose a new variational Bayes algorithm which generalizes and improves the recently developed “sum of single effects” model of Wang et al. (2020a). Motivated by differential gene network analysis in biology, we further extend our method to joint structure learning of multiple directed acyclic graphical models, a problem known to be computationally highly challenging. We propose a novel order MCMC sampler where our multi-task variable selection algorithm is used to quickly evaluate the posterior probability of each ordering. Both simulation studies and real gene expression data analysis are conducted to show the efficiency of our method. Finally, we also prove a posterior consistency result for multi-task variable selection, which provides a theoretical guarantee for the proposed algorithms. Supplementary materials for this article are available online.
DOI: 10.21233/zs99-7z37
title: CNTA10 pollen surface sample dataset

description: Raw data for the CNTA10 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.24047050.v1
title: Bayesian Multi-task Variable Selection with an Application to Differential DAG Analysis

description: We study the Bayesian multi-task variable selection problem, where the goal is to select activated variables for multiple related data sets simultaneously. We propose a new variational Bayes algorithm which generalizes and improves the recently developed “sum of single effects” model of Wang et al. (2020a). Motivated by differential gene network analysis in biology, we further extend our method to joint structure learning of multiple directed acyclic graphical models, a problem known to be computationally highly challenging. We propose a novel order MCMC sampler where our multi-task variable selection algorithm is used to quickly evaluate the posterior probability of each ordering. Both simulation studies and real gene expression data analysis are conducted to show the efficiency of our method. Finally, we also prove a posterior consistency result for multi-task variable selection, which provides a theoretical guarantee for the proposed algorithms. Supplementary materials for this article are available online.
DOI: 10.21233/ka3j-z510
title: CNTA11 pollen surface sample dataset

description: Raw data for the CNTA11 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0g11-1964
title: CNTA12 pollen surface sample dataset

description: Raw data for the CNTA12 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/jhex-s542
title: CNTA13 pollen surface sample dataset

description: Raw data for the CNTA13 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.7216238
title: Additional file 1: of Mitochondrial DNA 7908â 8816 region mutations in maternally inherited essential hypertensive subjects in China

description: Online mtDNA mutations of the MIEH patients. (RAR 3557 kb)
DOI: 10.21233/1t6w-vr35
title: CNTA14 pollen surface sample dataset

description: Raw data for the CNTA14 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/j9j8-zg91
title: CNTA15 pollen surface sample dataset

description: Raw data for the CNTA15 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.7216238.v1
title: Additional file 1: of Mitochondrial DNA 7908â 8816 region mutations in maternally inherited essential hypertensive subjects in China

description: Online mtDNA mutations of the MIEH patients. (RAR 3557 kb)
DOI: 10.21233/x8t3-4759
title: FKIN5 pollen surface sample dataset

description: Raw data for the FKIN5 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/99rw-ea32
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/erpp-7j43
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/zsjj-hd02
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/49rb-m949
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/maej-h417
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/x23a-ty72
title: Lake Tenndammen pollen dataset

description: Raw data for the Lake Tenndammen pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/35re-d231
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0xr2-6360
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/p09r-zw73
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/dfrt-w704
title: Gull Lake diatom surface sample dataset

description: Raw data for the Gull Lake diatom surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080684
title: Repeatability and Reproducibility of Anterior Chamber Angle Measurement with Swept-Source Optical Coherence Tomography in Patients with Primary Angle Closure Suspect

description: <b>Purpose</b>: To evaluate the inter- and intra-observer reliability of anterior chamber (AC) angle measurements obtained by swept-source optical coherence tomography (SS-OCT). <b>Methods:</b> Forty-eight consecutive patients diagnosed with primary angle closure suspect (PACS) were included. Three masked observers at different training levels (one glaucoma specialist, one ophthalmology resident, and one pre-medical college student) measured 192 SS-OCT images of the PACS patients. One observer (the glaucoma specialist) repeated measurements 1 week later. SS-OCT parameters included: Anterior segment volume, including corneal, AC, and iris volume; anterior segment dimensions, including AC depth and width (ACD, ACW), and lens vault (LV); and angle parameters, including angle opening distance (AOD), angle recess area (ARA), trabecular iris space area (TISA), and the trabecular iris angle (TIA). Intraclass correlation coefficients (ICCs) were used to measure reliability. <b>Results:</b> For inter-observer reproducibility, ICCs of corneal, AC, and iris volumes were 0.952 to 0.998. ICCs of ACD at all axes were above 0.989. ICCs of ACW and LV were smallest in the 90°-270° axis (0.751 and 0.768) but not significantly different from other axes. ARA, TISA, and TIA at all angles had significantly smallest ICCs 250 µm from the scleral spur compared with 500 µm and 750 µm. The ICCs comparing observers with different training levels had similar ranges and followed similar trends. For intra-observer repeatability, the smallest ICC was 0.843. Decreasing AC depth correlated with increased inter-observer reproducibility. <b>Conclusions:</b> We found excellent intra-observer repeatability for all SS-OCT parameters. Angle measurements have more variation among the observers when taken 250 µm from the scleral spur. Shallow AC might lead to more variability for angle parameters. Non-expert observers may be recruited for high-quality image grading with standard training.
DOI: 10.21233/8pb4-bp29
title: Melaleuca Inlet pollen dataset

description: Raw data for the Melaleuca Inlet pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.15080684.v1
title: Repeatability and Reproducibility of Anterior Chamber Angle Measurement with Swept-Source Optical Coherence Tomography in Patients with Primary Angle Closure Suspect

description: <b>Purpose</b>: To evaluate the inter- and intra-observer reliability of anterior chamber (AC) angle measurements obtained by swept-source optical coherence tomography (SS-OCT). <b>Methods:</b> Forty-eight consecutive patients diagnosed with primary angle closure suspect (PACS) were included. Three masked observers at different training levels (one glaucoma specialist, one ophthalmology resident, and one pre-medical college student) measured 192 SS-OCT images of the PACS patients. One observer (the glaucoma specialist) repeated measurements 1 week later. SS-OCT parameters included: Anterior segment volume, including corneal, AC, and iris volume; anterior segment dimensions, including AC depth and width (ACD, ACW), and lens vault (LV); and angle parameters, including angle opening distance (AOD), angle recess area (ARA), trabecular iris space area (TISA), and the trabecular iris angle (TIA). Intraclass correlation coefficients (ICCs) were used to measure reliability. <b>Results:</b> For inter-observer reproducibility, ICCs of corneal, AC, and iris volumes were 0.952 to 0.998. ICCs of ACD at all axes were above 0.989. ICCs of ACW and LV were smallest in the 90°-270° axis (0.751 and 0.768) but not significantly different from other axes. ARA, TISA, and TIA at all angles had significantly smallest ICCs 250 µm from the scleral spur compared with 500 µm and 750 µm. The ICCs comparing observers with different training levels had similar ranges and followed similar trends. For intra-observer repeatability, the smallest ICC was 0.843. Decreasing AC depth correlated with increased inter-observer reproducibility. <b>Conclusions:</b> We found excellent intra-observer repeatability for all SS-OCT parameters. Angle measurements have more variation among the observers when taken 250 µm from the scleral spur. Shallow AC might lead to more variability for angle parameters. Non-expert observers may be recruited for high-quality image grading with standard training.
DOI: 10.21233/n83x-jx34
title: KG 951 VC 1 pollen dataset

description: Raw data for the KG 951 VC 1 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ej56-9k78
title: Pian del Lago microcharcoal dataset

description: Raw data for the Pian del Lago microcharcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0xhw-cw57
title: GATC3 pollen surface sample dataset

description: Raw data for the GATC3 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/1v8c-e642
title: GATC5 pollen surface sample dataset

description: Raw data for the GATC5 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0cza-rz61
title: GATC6 pollen surface sample dataset

description: Raw data for the GATC6 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/sn68-8t89
title: GATC8 pollen surface sample dataset

description: Raw data for the GATC8 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/bxzt-0n48
title: GATC9 pollen surface sample dataset

description: Raw data for the GATC9 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/k1ej-rs20
title: GATC10 pollen surface sample dataset

description: Raw data for the GATC10 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/q97m-4j72
title: GATC11 pollen surface sample dataset

description: Raw data for the GATC11 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/mva7-s133
title: GATC12 pollen surface sample dataset

description: Raw data for the GATC12 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/x53f-zv95
title: GATC13 pollen surface sample dataset

description: Raw data for the GATC13 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/61rz-aj32
title: GATC14 pollen surface sample dataset

description: Raw data for the GATC14 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/dsf2-mc02
title: GATC15 pollen surface sample dataset

description: Raw data for the GATC15 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/9vm5-a916
title: GATC16 pollen surface sample dataset

description: Raw data for the GATC16 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/msj5-3p61
title: Stagno di Chia pollen dataset

description: Raw data for the Stagno di Chia pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/967d-q473
title: Stagno di Chia charcoal dataset

description: Raw data for the Stagno di Chia charcoal dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0vgb-5s79
title: GATC17 pollen surface sample dataset

description: Raw data for the GATC17 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/1vp6-k629
title: GATC21 pollen surface sample dataset

description: Raw data for the GATC21 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/tsfh-gq38
title: GATC22 pollen surface sample dataset

description: Raw data for the GATC22 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/ev29-gh57
title: GATC23 pollen surface sample dataset

description: Raw data for the GATC23 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/wgns-dd43
title: GATC24 pollen surface sample dataset

description: Raw data for the GATC24 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/pxkm-c707
title: GATC25 pollen surface sample dataset

description: Raw data for the GATC25 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/7kqz-h594
title: GATC26 pollen surface sample dataset

description: Raw data for the GATC26 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/h5f5-vc04
title: GATC27 pollen surface sample dataset

description: Raw data for the GATC27 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/d60p-y026
title: GATC28 pollen surface sample dataset

description: Raw data for the GATC28 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/0pbz-z834
title: GATC29 pollen surface sample dataset

description: Raw data for the GATC29 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/qkw5-kn67
title: GATC30 pollen surface sample dataset

description: Raw data for the GATC30 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.7216517
title: Additional file 2: of High-flow nasal cannula in adults with acute respiratory failure and after extubation: a systematic review and meta-analysis

description: Additional Tables and Figures. (ZIP 5033 kb)
DOI: 10.21233/yvqg-mm51
title: GATC31 pollen surface sample dataset

description: Raw data for the GATC31 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/kgc6-kb71
title: GATC32 pollen surface sample dataset

description: Raw data for the GATC32 pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.6084/m9.figshare.7216517.v1
title: Additional file 2: of High-flow nasal cannula in adults with acute respiratory failure and after extubation: a systematic review and meta-analysis

description: Additional Tables and Figures. (ZIP 5033 kb)