cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5122831
title: CINECA synthetic cohort NA Canada CHILD [CC-BY-NC-SA]

description: The "CINECA synthetic cohort NA Canada CHILD" dataset is a synthetic dataset developed to provide insight into how data is structured for select common attributes in the CHILD Cohort Study, but not reveal any personal or identifiable information associated with cohort participants. Such synthetic datasets are valuable for software developers to be able to see specific examples of data for common attributes (i.e. a minimal metadata model of a selection of common variables usually present in cohorts). This dataset comprises 100 variables for 150 synthetic participants which have faked phenotypic data that reflects CHILD cohort data. In addition, there is genetic data based on the 1000 Genomes project. This dataset was created within the context of the CINECA project. More information about the creation of this dataset can be found in the included documentation. <br> <em>Please note this preamble must be included with any distribution of this dataset: </em>This synthetic dataset (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about CHILD cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY; https://creativecommons.org/licenses/by-nc-sa/4.0/). Please ensure this preamble is included with this dataset and that the CHILD project and the CINECA project (funding: EC H2020 grant 825775 and CIHR grant 404896) are acknowledged. If you have any questions about this dataset contact Fiona Brinkman at brinkman@sfu.ca or Erin Gill at egill@sfu.ca. <strong>CINECA synthetic cohorts</strong> CINECA synthetic cohort Africa H3ABioNet CINECA synthetic cohort Europe CH SIB CINECA synthetic cohort Europe UK1
DOI: 10.6069/yq43-1q11
title: Global Expected Health Spending 2019-2050

description: Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2019-2050 for 204 countries and territories. The estimates cover total health spending, health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private), and development assistance for health (DAH). Retrospective health spending estimates for 1995-2018 and key covariates (including GDP per capita, total government spending, total fertility rate, and fraction of the population older than 65 years) were used to forecast GDP and health spending through 2050. Estimates are reported in constant 2020 US dollars, constant 2020 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.
DOI: 10.5281/zenodo.5534855
title: Dataset for CuttingEEG 2021 - NeuroPycon tutorial

description: Datset for the NeuroPycon tutorial at CuttingEEG 2021 ERP_CORE -&gt; EEG dataset ds000117 -&gt; MEG dataset For installation and info look at https://neuropycon.github.io/ephypype/auto_workshop
DOI: 10.5281/zenodo.5534854
title: Dataset for CuttingEEG 2021 - NeuroPycon tutorial

description: Datset for the NeuroPycon tutorial at CuttingEEG 2021 ERP_CORE -&gt; EEG dataset ds000117 -&gt; MEG dataset For installation and info look at https://neuropycon.github.io/ephypype/auto_workshop
DOI: 10.6069/a5v8-5817
title: Development Assistance for Health on COVID-19 2020

description: Development Assistance for Health (DAH) on COVID-19 produced estimates for 2020, which are based on project databases, financial statements, annual reports, IRS 990s, and correspondence with agencies. The DAH Database enables comprehensive analysis of trends in international disbursements of grants and loans for COVID-19-related health projects in low- and middle-income countries from key agencies. The data are disaggregated by source of funds, channel of funding, country and geographic region, health focus areas, and program areas.
DOI: 10.6084/m9.figshare.13148117.v7
title: Rawdata Baseline/Replication - Impact of Usability Mechanisms

description: Rawdata and R code for usability mechanism - analysis of impact.<br>Baseline.xlsx: raw data of baseline experiment.Replication_1.xlsx: raw data of replication #1 of baseline experimentReplication_2.xlsx: raw data of replication #2 of baseline experiment<br><br>Analysis.R: file containing the R code to analyze experiments (Summary statistics).Meta-analysis.R: file containing the R code to analyze SE family of experiments.
DOI: 10.6069/7f1n-ct45
title: Global Health Spending 1995-2018

description: Research by the Global Burden of Disease Health Financing Collaborator Network produced retrospective health spending estimates for 1995-2018 for 204 countries and territories. They cover total health spending, health spending disaggregated by source into three domestic financing source categories, and development assistance for health (DAH). Domestic source data came primarily from the WHO’s Global Health Expenditure Database (GHED). DAH data came from diverse sources, including program reports, budget data, national estimates, and National Health Accounts (NHAs). The resulting estimates were used to forecast GDP and prospective health spending estimates for 2018-2050. Estimates are reported in constant 2020 United States dollars, constant 2020 purchasing power parity adjusted (PPP) dollars, and as a percent of gross domestic product.
DOI: 10.6069/wc70-fc19
title: Gross Domestic Product Per Capita 1960-2050

description: Research by the Global Burden of Disease Health Financing Collaborator Network produced estimates for Gross Domestic Product (GDP) from 1960-2050. Estimates are reported as GDP per person in constant 2020 purchasing-power parity-adjusted (PPP) dollars.
DOI: 10.5517/ccdc.csd.cc28j8y1
title: CCDC 2101023: Experimental Crystal Structure Determination

description: Related Article: Jiaqi Dong, Lutao Zhang, Dehui Tan, Jianfeng Wu, Nan Wang, Soren K. Mellerup, Suning Wang, Deng-Tao Yang|2021|Chem.Commun.|57|9882|doi:10.1039/D1CC04228A
DOI: 10.5517/ccdc.csd.cc28jgs2
title: CCDC 2101204: Experimental Crystal Structure Determination

description: Related Article: R. Sibille, T. Mazet, L.V.B. Diop, M. François|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|801|doi:10.1107/S2052520621007988
DOI: 10.5517/ccdc.csd.cc1pm2s5
title: CCDC 1567477: Experimental Crystal Structure Determination

description: Related Article: Adish Tyagi, Gourab Karmakar, B. P. Mandal, Dipa Dutta Pathak, Amey Wadawale, G. Kedarnath, A. P. Srivastava, Vimal K. Jain|2021|Dalton Trans.|50|13073|doi:10.1039/D1DT01142A
DOI: 10.5517/ccdc.csd.cc1pm2v7
title: CCDC 1567479: Experimental Crystal Structure Determination

description: Related Article: Adish Tyagi, Gourab Karmakar, B. P. Mandal, Dipa Dutta Pathak, Amey Wadawale, G. Kedarnath, A. P. Srivastava, Vimal K. Jain|2021|Dalton Trans.|50|13073|doi:10.1039/D1DT01142A
DOI: 10.5517/ccdc.csd.cc1pm2w8
title: CCDC 1567480: Experimental Crystal Structure Determination

description: Related Article: Adish Tyagi, Gourab Karmakar, B. P. Mandal, Dipa Dutta Pathak, Amey Wadawale, G. Kedarnath, A. P. Srivastava, Vimal K. Jain|2021|Dalton Trans.|50|13073|doi:10.1039/D1DT01142A
DOI: 10.25505/fiz.icsd.cc28jrcy
title: CSD 2101470: Experimental Crystal Structure Determination

description: Related Article: Z. Luo|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|772|doi:10.1107/S2052520621008027
DOI: 10.5517/ccdc.csd.cc27fx3q
title: CCDC 2068943: Experimental Crystal Structure Determination

description: Related Article: Wenzeng Duan, Honghan Ji, Zeren Yang, Qingxia Yao, Yanmin Huo, Xuerui Ren, Jiaojiao Zhao, Shuwen Gong|2021|Dalton Trans.|50|12963|doi:10.1039/D1DT02231H
DOI: 10.5517/ccdc.csd.cc27fx7v
title: CCDC 2068947: Experimental Crystal Structure Determination

description: Related Article: Wenzeng Duan, Honghan Ji, Zeren Yang, Qingxia Yao, Yanmin Huo, Xuerui Ren, Jiaojiao Zhao, Shuwen Gong|2021|Dalton Trans.|50|12963|doi:10.1039/D1DT02231H
DOI: 10.5517/ccdc.csd.cc27fxby
title: CCDC 2068950: Experimental Crystal Structure Determination

description: Related Article: Wenzeng Duan, Honghan Ji, Zeren Yang, Qingxia Yao, Yanmin Huo, Xuerui Ren, Jiaojiao Zhao, Shuwen Gong|2021|Dalton Trans.|50|12963|doi:10.1039/D1DT02231H
DOI: 10.5517/ccdc.csd.cc27fxd0
title: CCDC 2068952: Experimental Crystal Structure Determination

description: Related Article: Wenzeng Duan, Honghan Ji, Zeren Yang, Qingxia Yao, Yanmin Huo, Xuerui Ren, Jiaojiao Zhao, Shuwen Gong|2021|Dalton Trans.|50|12963|doi:10.1039/D1DT02231H
DOI: 10.5517/ccdc.csd.cc265f61
title: CCDC 2031002: Experimental Crystal Structure Determination

description: Related Article: Tong Xu, Jiantang Li, Mingwei Jia, Guanghua Li, Yunling Liu|2021|Dalton Trans.|50|13096|doi:10.1039/D1DT00947H
DOI: 10.5517/ccdc.csd.cc28bt0g
title: CCDC 2095755: Experimental Crystal Structure Determination

description: Related Article: Min Li, Xinli Liu, Dongmei Cui|2021|Dalton Trans.|50|13037|doi:10.1039/D1DT00143D
DOI: 10.5517/ccdc.csd.cc280f2t
title: CCDC 2085775: Experimental Crystal Structure Determination

description: Related Article: Peng Hu, Ling-hui Cao, Ao-gang Liu, Yi-quan Zhang, Tian-le Zhang, Bao Li|2021|Dalton Trans.|50|12814|doi:10.1039/D1DT02380B
DOI: 10.5517/ccdc.csd.cc283sz5
title: CCDC 2089027: Experimental Crystal Structure Determination

description: Related Article: Wiebke Drescher, Christian Kleeberg|2021|Dalton Trans.|50|13149|doi:10.1039/D1DT02327F
DOI: 10.5281/zenodo.5534880
title: Supplementary file 1

description: Supplementary file of the paper: 'The utility of cytomorphometry in the diagnostics of chronic nasal diseases in dogs. A preliminary study.' Diana Stopka, Michał Czopowicz, Magdalena Ostrzeszewicz, Rafał Sapierzyński.
DOI: 10.5281/zenodo.4947158
title: Supplementary file 1

description: Supplementary file of the paper: 'The utility of cytomorphometry in the diagnostics of chronic nasal diseases in dogs. A preliminary study.' Diana Stopka, Michał Czopowicz, Magdalena Ostrzeszewicz, Rafał Sapierzyński.
DOI: 10.18112/openneuro.ds003800.v1.0.0
title: Auditory Gamma Entrainment
DOI: 10.18112/openneuro.ds003805.v1.0.0
title: Multisensory Gamma Entrainment
DOI: 10.6084/m9.figshare.16695376
title: Graphs not solved in stages 0 and 1.zip

description: Graphs not solved in stages 0 and 1
DOI: 10.6084/m9.figshare.16695376.v1
title: Graphs not solved in stages 0 and 1.zip

description: Graphs not solved in stages 0 and 1
DOI: 10.6084/m9.figshare.3761562.v1995
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.2551813
title: Data sets associated with "Deep structure of the lunar South Pole–Aitken basin"

description: These grid files contain the data used in Figures 2 and 3 of the submitted manuscript, "Deep structure of the lunar South Pole–Aitken basin". Although the data are global, note that they are not meant to be used outside of the South Pole–Aitken basin.
DOI: 10.5281/zenodo.2551812
title: Data sets associated with "Deep structure of the lunar South Pole–Aitken basin"

description: These grid files contain the data used in Figures 2 and 3 of the submitted manuscript, "Deep structure of the lunar South Pole–Aitken basin". Although the data are global, note that they are not meant to be used outside of the South Pole–Aitken basin.
DOI: 10.25345/c5kn9r
title: MassIVE MSV000088168 - Stress-induced DLK-vesicle assembly acts as local signaling platform that drives kinase activation and neurodegeneration
title: Mitogen-activated protein kinases (MAPKs) drive key signaling cascades during neuronal survival and degeneration. The re-localization of kinases to specific subcellular compartments is a critical mechanism to locally control signaling activity and specificity upon stimulation. However, how MAPK signaling components tightly control their localization remains largely unknown. Here, we systematically analyzed the phosphorylation and membrane localization of all MAPKs expressed in dorsal root ganglia neurons, under control and stress conditions. We found that MAP3K12/ dual leucine zipper kinase (DLK) is the only MAPK that becomes phosphorylated and palmitoylated, and it is recruited to sphingomyelin rich vesicles upon stress. The DLK stress-induced vesicle assembly is essential for kinase activation; blocking DLK-membrane interactions inhibits downstream signaling, while DLK recruitment to ectopic subcellular structures is sufficient to induce kinase activation. We show that the localization of DLK to newly formed vesicles is essential for local signaling and inhibition of membrane internalization blocks DLK activation and protects against neurodegeneration. These data establish vesicular assemblies as dynamically regulated platforms for DLK signaling during neuronal stress responses.
DOI: 10.5281/zenodo.5534905
title: Additional data "Client binding shifts the populations of dynamic Hsp90 conformations through an allosteric network"

description: Additional data containing chemical shift perturbations and intensity changes of Hsp90 upon client binding, intermolecular PREs, and raw scattering data of Hsp90-client complexes.
DOI: 10.5281/zenodo.5534904
title: Additional data "Client binding shifts the populations of dynamic Hsp90 conformations through an allosteric network"

description: Additional data containing chemical shift perturbations and intensity changes of Hsp90 upon client binding, intermolecular PREs, and raw scattering data of Hsp90-client complexes.
DOI: 10.6084/m9.figshare.14794596.v2
title: Tmem43 Suppl. Materials-06.15.2021-AJP.docx

description: Suppl. Material
DOI: 10.5066/p99etdl5
title: Soil physical, chemical, and biological data from edge-of-field agricultural water quality monitoring sites in Great Lakes States

description: Soil data were collected from catchments of USGS edge-of-field (EOF) monitoring sites in Great Lakes Restoration Initiative (GLRI) priority watersheds. As part of this release, soil data from 2016 through 2019 are provided from 14 sites spanning 5 Great Lake States (Wisconsin, Michigan, Ohio, Indiana, and New York). The data collected are from private farms representing a variety of agronomic systems, landscapes settings, soil types, and climate regimes. These data can be used to investigate relationships among microbial properties (e.g. soil microbial biomass, activity, diversity, and enzymes), general soil structure (e.g. bulk density, soil aggregate structure, soil water holding capacity, soil texture, and infiltration rates), soil resources (e.g. organic matter, reactive carbon, total carbon, nitrogen, water extractable phosphorus, cations), and exported resources (e.g. runoff volume, phosphorus loads). Water quality data, site descriptions, and management are provided in a companion data release (Komiskey and others, 2021).
DOI: 10.6084/m9.figshare.14794596
title: Tmem43 Suppl. Materials-AJP.docx

description: Suppl. Material
DOI: 10.6084/m9.figshare.16695448.v1
title: Graphs not solved in all stages.zip

description: Graphs not solved in all stages.
DOI: 10.6084/m9.figshare.16695448
title: Graphs not solved in all stages.zip

description: Graphs not solved in all stages.
DOI: 10.18739/a2vx0643z
title: Arctic Subpolar gyre sTate Estimate, Release 1 (ASTE_R1) profile data 2002 - 2017

description: Output of the Arctic Subpolar gyre sTate Estimate, Release 1 (ASTE_R1) (Nguyen et al., JAMES, 2021). The product was achieved through adjoint-based nonlinear least-squares optimization, whereby the misfit of the model to an extensive (O(10^9)) set of satellite and in situ observations was iteratively minimized. The primary product consists of monthly means of state variables, as well as monthly mean and snapshot fields required for accurate budget calculations. The ASTE_R1 product is structured as follows: 1. Time varying fields as monthly averages (nctiles_monthly) and monthly snapshots (nctiles_monthly_snapshots), doi:10.18739/A2CV4BS5IK 2. Depth-integrated time varying fields as monthly averages (nctiles_monthly_integrated) and monthly snapshots (nctiles_monthly_snapshots_integrated), doi:10.18739/A2833N015 3. Selected time varying state variables as daily averages (nctiles_daily), doi:10.18739/A24B2X58W 4. 12-month climatological averages (nctiles_climatology), doi:10.18739/A20P0WR8J 5. Vertical profiles of in-situ measurements and model-equivalent (profiles), doi:10.18739/A2VX0643Z 6. ASTE_R1 grid files (nctiles_grid), documentation (user guide, domain layout), and MATLAB toolbox to help analyze the output fields, doi:10.18739/A2MC8RH3V This dataset (5) contains the model equivalent of vertical profiles of in situ data in NetCDF format. The model fields are sampled on the fly during model integration at the time and location of the in situ data to generate the model equivalent. See Section 4.9 in the ASTE_R1 User Guide at doi:10.18739/A2MC8RH3V for more details.
DOI: 10.6082/uchicago.3323
title: Rare Event Sampling Improves Mercury Instability Statistics

description: This tgz file contains data and code to reproduce the results from the paper "Rare Event Sampling Improves Mercury Instability Statistics" https://arxiv.org/abs/2106.09091
DOI: 10.5281/zenodo.5532805
title: Constrained Fitness Landscape Analysis of Vehicle Routing Problems

description: The repository is a set of Jupyter notebooks and datasets used in the article titled: “Constrained Fitness Landscape Analysis for Capacitated Vehicle routing problems.” The main objective is to give the tools for reproducing the results shown in the paper.
DOI: 10.5281/zenodo.5532804
title: Constrained Fitness Landscape Analysis of Vehicle Routing Problems

description: The repository is a set of Jupyter notebooks and datasets used in the article titled: “Constrained Fitness Landscape Analysis for Capacitated Vehicle routing problems.” The main objective is to give the tools for reproducing the results shown in the paper.
DOI: 10.5281/zenodo.5525047
title: Dataset and Code for "Bayesian spline method for assessing extreme loads on wind turbines"

description: Here are two files. One file contains the datasets and the other contains the computer code used to generate the results in the paper, Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” <em> Annals of Applied Statistics</em>, Vol. 7, pp. 2034-2061.
description: {"references": ["Lee, Byon, Ntaimo, and Ding, 2013, \"Bayesian spline method for assessing extreme loads on wind turbines,\" Annals of Applied Statistics, Vol. 7, pp. 2034-2061"]}
DOI: 10.5281/zenodo.5525046
title: Dataset and Code for "Bayesian spline method for assessing extreme loads on wind turbines"

description: Here are two files. One file contains the datasets and the other contains the computer code used to generate the results in the paper, Lee, Byon, Ntaimo, and Ding, 2013, “Bayesian spline method for assessing extreme loads on wind turbines,” <em> Annals of Applied Statistics</em>, Vol. 7, pp. 2034-2061.
description: {"references": ["Lee, Byon, Ntaimo, and Ding, 2013, \"Bayesian spline method for assessing extreme loads on wind turbines,\" Annals of Applied Statistics, Vol. 7, pp. 2034-2061"]}
DOI: 10.5281/zenodo.5525065
title: Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"

description: This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.
description: {"references": ["Lee, Ding, Genton, and Xie, 2015, \"Power curve estimation with multivariate environmental factors for inland and offshore wind farms,\" Journal of the American Statistical Association, Vol. 110, pp. 56-67"]}
DOI: 10.5281/zenodo.5525064
title: Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"

description: This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.
description: {"references": ["Lee, Ding, Genton, and Xie, 2015, \"Power curve estimation with multivariate environmental factors for inland and offshore wind farms,\" Journal of the American Statistical Association, Vol. 110, pp. 56-67"]}
DOI: 10.6084/m9.figshare.12724664
title: Figure 1A β-gal analysis comparing young vs aged MAECs cultured in normal vs. high glucose

description: Fig. 1A.
DOI: 10.6084/m9.figshare.12724664.v1
title: Figure 1A β-gal analysis comparing young vs aged MAECs cultured in normal vs. high glucose

description: Fig. 1A.
DOI: 10.6084/m9.figshare.16695553
title: sesh_21-07-20_165209.zip

description: The dataset used to generate the animation seen here - https://twitter.com/JonMatthis/status/1417864732749582337<br>Code repo here - github.com/jonmatthis/freemocap<br>Specifically, this is the function used to generate the animation -<br>https://github.com/jonmatthis/freemocap/blob/main/freemocap/play_skeleton_animation.py<br>(Currently that animation doesn't work on other-people's computers, I think ...)<br>Details about the project here - jonmatthis.com/fmc.html<br>All still a work in progress! Stay tuned on the project for more!<br>See the 'Sample Data' section of the GitHub wiki for details - https://github.com/jonmatthis/freemocap/wiki#sample-data
DOI: 10.6084/m9.figshare.16695553.v1
title: sesh_21-07-20_165209.zip

description: The dataset used to generate the animation seen here - https://twitter.com/JonMatthis/status/1417864732749582337<br>Code repo here - github.com/jonmatthis/freemocap<br>Specifically, this is the function used to generate the animation -<br>https://github.com/jonmatthis/freemocap/blob/main/freemocap/play_skeleton_animation.py<br>(Currently that animation doesn't work on other-people's computers, I think ...)<br>Details about the project here - jonmatthis.com/fmc.html<br>All still a work in progress! Stay tuned on the project for more!<br>See the 'Sample Data' section of the GitHub wiki for details - https://github.com/jonmatthis/freemocap/wiki#sample-data