cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12162447
title: GTT-1-protein-018.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162456.v1
title: GTT-1-protein-020.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162462.v1
title: GTT-1-protein-022.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162444
title: GTT-1-protein-017.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162447.v1
title: GTT-1-protein-018.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162453.v1
title: GTT-1-protein-019.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162435
title: GTT-1-protein-015.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162441
title: GTT-1-protein-016.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162444.v1
title: GTT-1-protein-017.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162426
title: GTT-1-protein-013.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162432
title: GTT-1-protein-014.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162441.v1
title: GTT-1-protein-016.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162423
title: GTT-1-protein-012.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162426.v1
title: GTT-1-protein-013.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162432.v1
title: GTT-1-protein-014.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162420
title: GTT-1-protein-011.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162435.v1
title: GTT-1-protein-015.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162414
title: GTT-1-protein-009.dcd

description: GromacsWW domain trajectoriesTrajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162417
title: GTT-1-protein-010.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162411
title: GTT-1-protein-008.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162408
title: GTT-1-protein-007.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162405
title: GTT-1-protein-006.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162411.v1
title: GTT-1-protein-008.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.34740/kaggle/dsv/1441877
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.12162402
title: GTT-1-protein-005.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162405.v1
title: GTT-1-protein-006.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162399
title: GTT-1-protein-004.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162402.v1
title: GTT-1-protein-005.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162408.v1
title: GTT-1-protein-007.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162387
title: GTT-1-protein-002.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162390
title: GTT-1-protein-003.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162414.v1
title: GTT-1-protein-009.dcd

description: Gromacs
DOI: 10.6084/m9.figshare.12162399.v1
title: GTT-1-protein-004.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162387.v1
title: GTT-1-protein-002.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162381
title: GTT-1-protein.mae

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162384
title: GTT-1-protein-001.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162414.v3
title: GTT-1-protein-009.dcd

description: GromacsWW domain trajectoriesTrajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162375
title: gtt-1-protein.pdb

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162378
title: GTT-1-protein-000.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162384.v1
title: GTT-1-protein-001.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162378.v1
title: GTT-1-protein-000.dcd

description: Trajectories dataTrajectories of WW domain
DOI: 10.6084/m9.figshare.12162414.v2
title: GTT-1-protein-009.dcd

description: GromacsWW domain trajectories
DOI: 10.6084/m9.figshare.12162381.v1
title: GTT-1-protein.mae

description: Trajectories dataTrajectories of WW domain
DOI: 10.5522/04/12162354
title: Neuropixels recordings of retinal axons in mouse optic tract

description: These data contain activity (in form of action potentials) of single retinal axons recorded in the optic tract of mouse. Mice were able to run on rotating wheel. Running speed was recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), darkness (“dark”), black and white squares on gray background (“noise”), or full-field screen inverting between black and white at fast rates ("flickers").<br>See Schröder et al. (2020) for details on methods (https://www.cell.com/neuron/fulltext/S0896-6273(20)30318-4).<br>See Optic tract ALF definitions.pdf for detailed description of data.
DOI: 10.6084/m9.figshare.12162375.v1
title: gtt-1-protein.pdb

description: Trajectories dataTrajectories of WW domain
DOI: 10.5522/04/12162324
title: Two-photon imaging of neurons in mouse superior colliculus

description: These data contain activity (in form of calcium traces) of single neurons in superior colliculus (SC) of mouse. Mice were able to run on a floating ball. Running speed and pupil size were recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), uniform gray screens (“grayScreen”), or black and white squares on gray background (“noise”).See Schröder et al. (2020) for details on methods.See SC neurons 2P ALF definitions.pdf for detailed description of data.
DOI: 10.5522/04/12162354.v1
title: Neuropixels recordings of retinal axons in mouse optic tract

description: These data contain activity (in form of action potentials) of single retinal axons recorded in the optic tract of mouse. Mice were able to run on a floating ball. Running speed was recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), darkness (“dark”), black and white squares on gray background (“noise”), or full-field screen inverting between black and white at fast rates ("flickers").See Schröder et al. (2020) for details on methods.See Optic tract ALF definitions.pdf for detailed description of data.
DOI: 10.6084/m9.figshare.12162300
title: Metadata record for: GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

description: This dataset contains key characteristics about the data described in the Data Descriptor GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.5522/04/12162354.v2
title: Neuropixels recordings of retinal axons in mouse optic tract

description: These data contain activity (in form of action potentials) of single retinal axons recorded in the optic tract of mouse. Mice were able to run on a floating ball. Running speed was recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), darkness (“dark”), black and white squares on gray background (“noise”), or full-field screen inverting between black and white at fast rates ("flickers").See Schröder et al. (2020) for details on methods.See Optic tract ALF definitions.pdf for detailed description of data.
DOI: 10.5522/04/12162324.v1
title: Two-photon imaging of neurons in mouse superior colliculus (.npy)

description: These data contain activity (in form of calcium traces) of single neurons in superior colliculus (SC) of mouse. Mice were able to run on a floating ball. Running speed and pupil size were recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), uniform gray screens (“grayScreen”), or black and white squares on gray background (“noise”).See Schröder et al. (2020) for details on methods.See SC neurons 2P ALF definitions.pdf for detailed description of data.
DOI: 10.5522/04/12162324.v3
title: Two-photon imaging of neurons in mouse superior colliculus

description: These data contain activity (in form of calcium traces) of single neurons in superior colliculus (SC) of mouse. Mice were able to run on a floating ball. Running speed and pupil size were recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), uniform gray screens (“grayScreen”), or black and white squares on gray background (“noise”).See Schröder et al. (2020) for details on methods.See SC neurons 2P ALF definitions.pdf for detailed description of data.
DOI: 10.5522/04/12162324.v2
title: Two-photon imaging of neurons in mouse superior colliculus

description: These data contain activity (in form of calcium traces) of single neurons in superior colliculus (SC) of mouse. Mice were able to run on a floating ball. Running speed and pupil size were recorded simultaneously. Mice were presented with full-field, drifting, sinusoidal gratings (“gratings”), uniform gray screens (“grayScreen”), or black and white squares on gray background (“noise”).See Schröder et al. (2020) for details on methods.See SC neurons 2P ALF definitions.pdf for detailed description of data.
DOI: 10.6084/m9.figshare.12162147
title: Metadata record for: An improved high-quality genome assembly and annotation of Tibetan hulless barley

description: This dataset contains key characteristics about the data described in the Data Descriptor An improved high-quality genome assembly and annotation of Tibetan hulless barley. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br>
DOI: 10.6084/m9.figshare.12162147.v1
title: Metadata record for: An improved high-quality genome assembly and annotation of Tibetan hulless barley

description: This dataset contains key characteristics about the data described in the Data Descriptor An improved high-quality genome assembly and annotation of Tibetan hulless barley. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br>
DOI: 10.6084/m9.figshare.12162141
title: Supplementary Material for: Relationship between Gene Polymorphisms and Urine Iodine Levels on Susceptibility to Thyroid Peroxidase Antibody Positivity in the Chinese Population

description: <b><i>Background/Aims:</i></b> Hashimoto thyroiditis, characterized by positive thyroid peroxidase antibodies (TPOAbs), is caused by the interaction of genetic and environment factors. The aim of this study was to clarify the interaction of gene polymorphisms and iodine intake in the incidence of TPOAb positivity. <b><i>Methods:</i></b> 1,733 subjects were included in this study. Genomic DNA was extracted from peripheral blood white cells. Four SNPs (rs11675434 [TPO], rs3094228 [HCP5], rs9277555 [HLA-DPB1], and rs301799 [RERE]) were selected for genotyping. Weighted TPOAb genetic risk score (GRS) was calculated based on these 4 SNPs. Thyroid hormones and autoimmune antibodies (TPOAb and thyroglobulin antibody) were determined using the electrochemiluminescence immunoassay method. <b><i>Results:</i></b> The mean serum thyrotropin level in TPOAb-positive subjects was higher than in TPOAb-negative subjects (<i>p</i> &lt; 0.01). Genotype GG of rs9277555 was associated with an increased risk of TPOAb positivity (OR = 1.64, 5–95% CI 1.09, 2.47, <i>p</i> = 0.02). Genotype TT of rs11675434 showed marginal increased risk of TPOAb positivity (OR = 1.57, 5–95% CI 1.01, 2.43, <i>p</i> = 0.048). Logistic regression analysis showed TPOAb-GRS and rs9277555 were associated with TPOAb positivity (OR = 5.09, 5–95% CI 1.30, 19.91, <i>p</i> = 0.02 and OR = 1.30, 5–95% CI 1.05, 1.61, <i>p</i> = 0.02). Subjects with a high TPOAb-GRS had a 52% increased risk of TPOAb positivity compared to subjects with a low TPOAb-GRS (OR 1.52, 5–95% CI 1.05, 2.21, <i>p</i> = 0.03). <b><i>Conclusion:</i></b> TPOAb-GRS was associated with an increased risk of TPOAb positivity in a Chinese Han population. This effect might be attribute to rs9277555.
DOI: 10.6084/m9.figshare.12162090
title: Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice

description: Dataset from " Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice"<b></b>
DOI: 10.6084/m9.figshare.12162141.v1
title: Supplementary Material for: Relationship between Gene Polymorphisms and Urine Iodine Levels on Susceptibility to Thyroid Peroxidase Antibody Positivity in the Chinese Population

description: <b><i>Background/Aims:</i></b> Hashimoto thyroiditis, characterized by positive thyroid peroxidase antibodies (TPOAbs), is caused by the interaction of genetic and environment factors. The aim of this study was to clarify the interaction of gene polymorphisms and iodine intake in the incidence of TPOAb positivity. <b><i>Methods:</i></b> 1,733 subjects were included in this study. Genomic DNA was extracted from peripheral blood white cells. Four SNPs (rs11675434 [TPO], rs3094228 [HCP5], rs9277555 [HLA-DPB1], and rs301799 [RERE]) were selected for genotyping. Weighted TPOAb genetic risk score (GRS) was calculated based on these 4 SNPs. Thyroid hormones and autoimmune antibodies (TPOAb and thyroglobulin antibody) were determined using the electrochemiluminescence immunoassay method. <b><i>Results:</i></b> The mean serum thyrotropin level in TPOAb-positive subjects was higher than in TPOAb-negative subjects (<i>p</i> &lt; 0.01). Genotype GG of rs9277555 was associated with an increased risk of TPOAb positivity (OR = 1.64, 5–95% CI 1.09, 2.47, <i>p</i> = 0.02). Genotype TT of rs11675434 showed marginal increased risk of TPOAb positivity (OR = 1.57, 5–95% CI 1.01, 2.43, <i>p</i> = 0.048). Logistic regression analysis showed TPOAb-GRS and rs9277555 were associated with TPOAb positivity (OR = 5.09, 5–95% CI 1.30, 19.91, <i>p</i> = 0.02 and OR = 1.30, 5–95% CI 1.05, 1.61, <i>p</i> = 0.02). Subjects with a high TPOAb-GRS had a 52% increased risk of TPOAb positivity compared to subjects with a low TPOAb-GRS (OR 1.52, 5–95% CI 1.05, 2.21, <i>p</i> = 0.03). <b><i>Conclusion:</i></b> TPOAb-GRS was associated with an increased risk of TPOAb positivity in a Chinese Han population. This effect might be attribute to rs9277555.
DOI: 10.6084/m9.figshare.12162078
title: Experiments in the Wild: Introducing the Within-Person Encouragement Design

description: The within-person encouragement design introduced here combines methodological approaches from three research traditions: (a) the analysis of within-person couplings using multilevel models, (b) the experimental manipulation of a treatment variable at the within-person level, and (c) the use of random encouragements as instrumental variables to induce exogenous experimental variation when strict treatment adherence is unrealistic. The proposed combination of these approaches opens up new possibilities to study treatment effects of a broad range of behavioral variables in realistic everyday contexts. We introduce this new research design together with a corresponding data analysis framework: instrumental variable estimation with two-level structural equation models. Using simulations, we show that the approach is applicable with feasible design dimensions regarding numbers of measurement occasions and participants and realistic assumptions about adherence to the encouragement conditions. Possible applications and extensions, as well as potential problems and limitations are discussed.
DOI: 10.6084/m9.figshare.12162078.v1
title: Experiments in the Wild: Introducing the Within-Person Encouragement Design

description: The within-person encouragement design introduced here combines methodological approaches from three research traditions: (a) the analysis of within-person couplings using multilevel models, (b) the experimental manipulation of a treatment variable at the within-person level, and (c) the use of random encouragements as instrumental variables to induce exogenous experimental variation when strict treatment adherence is unrealistic. The proposed combination of these approaches opens up new possibilities to study treatment effects of a broad range of behavioral variables in realistic everyday contexts. We introduce this new research design together with a corresponding data analysis framework: instrumental variable estimation with two-level structural equation models. Using simulations, we show that the approach is applicable with feasible design dimensions regarding numbers of measurement occasions and participants and realistic assumptions about adherence to the encouragement conditions. Possible applications and extensions, as well as potential problems and limitations are discussed.
DOI: 10.6084/m9.figshare.12162069
title: Using Network Analysis for Examining Interpersonal Emotion Dynamics

description: Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing <i>inter</i>personal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners’ emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks’ interpersonal associations were predictive of partners’ relationship satisfaction over time only in Sample 2. We also include commented code implementing a new <i>dyadmlvar</i> R package developed for conducting this analysis.
DOI: 10.6084/m9.figshare.12162063
title: Trait data for Brambach genera

description: Trait data gathered from Flora Malesiana and other publications.
DOI: 10.6084/m9.figshare.12162063.v1
title: Trait data for Brambach genera

description: Trait data gathered from Flora Malesiana and other publications.
DOI: 10.6084/m9.figshare.12162069.v1
title: Using Network Analysis for Examining Interpersonal Emotion Dynamics

description: Several contemporary models conceptualize emotion as inherently interpersonal. We demonstrate how network analysis, a class of statistical methods often used to examine intrapersonal dynamic processes, provides a potential avenue for parameterizing <i>inter</i>personal emotion dynamics (and interpersonal dynamics in general). We claim that this method allows (a) observing interpersonal dynamics at various temporal levels; (b) examining interpersonal dynamics occurring through various emotional pathways; and (c) capturing variations in interpersonal networks, which can subsequently be used to predict changes in outcomes. To demonstrate the potential of this method, we used dyadic daily diary data on emotion dynamics from two samples; Sample 1 involved couples in their routine daily lives, whereas Sample 2 involved couples in their transition to parenthood. Graphical Multilevel-Vector-Autoregressive modeling was used to estimate partners’ emotional networks, whereas in a second step, LASSO was used to test the predictive value of couple-level differences of the obtained networks. The analysis revealed several patterns. For example, the between-couple network of Sample 1 was more interpersonally dense, but couple-level differences in the networks’ interpersonal associations were predictive of partners’ relationship satisfaction over time only in Sample 2. We also include commented code implementing a new <i>dyadmlvar</i> R package developed for conducting this analysis.
DOI: 10.6084/m9.figshare.12162090.v1
title: Mindfulness-based meditation and EQUA-S

description: Dataset from " Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice"<b></b>
DOI: 10.6084/m9.figshare.12162090.v3
title: Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice

description: Dataset from " Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice"<b></b>
DOI: 10.6084/m9.figshare.12162090.v2
title: Mindfulness-based meditation and EQUA-S

description: Dataset from " Trait and State Equanimity: The Effect of Mindfulness-Based Meditation Practice"<b></b>
DOI: 10.6084/m9.figshare.12162009
title: 2ORP-ligand_Babel.pdb

description: PDB Altered by Babel
DOI: 10.6084/m9.figshare.12161988
title: Supplemental Codes from How to learn to recognize conspecific brood parasitic offspring

description: R script to reproduce Figures 2 and 3.
DOI: 10.6084/m9.figshare.12161988.v1
title: Supplemental Codes from How to learn to recognize conspecific brood parasitic offspring

description: R script to reproduce Figures 2 and 3.
DOI: 10.6084/m9.figshare.12162009.v1
title: 2ORP-ligand_Babel.pdb

description: PDB Altered by Babel
DOI: 10.6084/m9.figshare.12161982
title: dataset files from Stress signalling in acellular slime moulds and its detection by conspecifics

description: zip file containing all datasets supporting our results
DOI: 10.6084/m9.figshare.12161979
title: Data and analysis script from Sensory bias and signal detection tradeoffs maintain intersexual floral mimicry

description: Zip file including datasets and R analysis script
DOI: 10.6084/m9.figshare.12161982.v1
title: dataset files from Stress signalling in acellular slime moulds and its detection by conspecifics

description: zip file containing all datasets supporting our results
DOI: 10.6084/m9.figshare.12161919
title: POWO March 2020 Southeast Asian plant distributions

description: Plant species distributions across TDWG level 3 regions in Southeast Asia. Received from Plants of the World Online database http://www.plantsoftheworldonline.org 6/3/20.
DOI: 10.6084/m9.figshare.12161919.v1
title: POWO March 2020 Southeast Asian plant distributions

description: Plant species distributions across TDWG level 3 regions in Southeast Asia. Received from Plants of the World Online database http://www.plantsoftheworldonline.org 6/3/20.
DOI: 10.6084/m9.figshare.12161979.v1
title: Data and analysis script from Sensory bias and signal detection tradeoffs maintain intersexual floral mimicry

description: Zip file including datasets and R analysis script
DOI: 10.6084/m9.figshare.12161910
title: Dataset.xlsx

description: This data-set consists raw data for a study on knowledge, attitude and practices of healthcare workers in Makerere University Teaching Hospitals towards Coronavirus Disease (COVID-19).