cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3901003
title: Output from oil drift simulations for NORSE19 experiment

description: Output from oil drift simulation simulations for the NORSE2019 oil-on-water experiment conducted in the North Sea June 2019. The data and experiment is described in Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020. The dataset consists of 4 netCDF files, corresponding to 4 simulations using different forcing/input data: - drifter_sailbuoy_wdf2.nc<br> Current from CODE drifter, 2% wind from sailbuoy, Stokes drift from forecast model<br> This is considered the reference simulation, with near perfect agreement with observed oil drift. - drifter_sailbuoy_wdf3.nc<br> Current from CODE drifter, 3% wind from sailbuoy, no Stokes drift- none_sailbuoy_wdf3.nc<br> No current or Stokes drift, only 3% of wind from sailbuoy - model_model_wdf2.nc<br> Current, wind (2%) and Stokes drift from forecast models. The data format is described on https://opendrift.github.io/
description: {"references": ["Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020."]}
DOI: 10.5281/zenodo.3901002
title: Output from oil drift simulations for NORSE19 experiment

description: Output from oil drift simulation simulations for the NORSE2019 oil-on-water experiment conducted in the North Sea June 2019. The data and experiment is described in Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020. The dataset consists of 4 netCDF files, corresponding to 4 simulations using different forcing/input data: - drifter_sailbuoy_wdf2.nc<br> Current from CODE drifter, 2% wind from sailbuoy, Stokes drift from forecast model<br> This is considered the reference simulation, with near perfect agreement with observed oil drift. - drifter_sailbuoy_wdf3.nc<br> Current from CODE drifter, 3% wind from sailbuoy, no Stokes drift- none_sailbuoy_wdf3.nc<br> No current or Stokes drift, only 3% of wind from sailbuoy - model_model_wdf2.nc<br> Current, wind (2%) and Stokes drift from forecast models. The data format is described on https://opendrift.github.io/
description: {"references": ["Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020."]}
DOI: 10.7910/dvn/3k6ooa/lkf1ho
title: Ravanilla_Haim_Hicken_BSNRC_R_Replication.R

description: :unav
DOI: 10.7910/dvn/3k6ooa/b99twh
title: main.tab

description: :unav
DOI: 10.7910/dvn/3k6ooa/o2yv2c
title: precinct.tab

description: :unav
DOI: 10.7910/dvn/3k6ooa/elb9fm
title: bgyNet_sparse.Rda

description: :unav
DOI: 10.7910/dvn/3k6ooa/dhrboq
title: do_sum_stats.do

description: :unav
DOI: 10.7910/dvn/3k6ooa/fwbfiz
title: Ravanilla_Haim_Hicken_BSNRC_Stata15_Replication.do

description: :unav
DOI: 10.7910/dvn/3k6ooa/tkyhln
title: bgyNet_dense.Rda

description: :unav
DOI: 10.7910/dvn/3k6ooa/tkrxov
title: main_matrix.tab

description: :unav
DOI: 10.7910/dvn/3k6ooa/b7ggvx
title: ReadMe.rtf

description: :unav
DOI: 10.7910/dvn/3k6ooa
title: Replication Data for Brokers, Social Networks, Reciprocity, and Clientelism

description: This dataset contains all files necessary to replicate the results reported in the manuscript titled, "Replication Data for Brokers, Social Networks, Reciprocity, and Clientelism."
DOI: 10.5281/zenodo.3901015
title: Catch me if you can. Can human observers identify insiders in asset markets?

description: Supplementary material This documentation provides information on the files and folders contained in the supplementary material. Note that all .do and .R files in the supplementary material are meant to be run directly from the folder containing them. 1.contracts\ 1.1.contracts\RegulationContractsModPas.do Contains calculations for the results reported in section 3.2 and for Table 5 of the paper. 2.globals\ Contains data potentially used in analysis. 3.infodata\ Contains data potentially used in analysis. 4.predictions\ 4.1.Predictions\Regulation_predictions.do Contains calculations for Tables 2-4 of the paper. 4.2.Predictions\Regulation_predictions_DetectionProb.do Contains calculations for the results reported in section 3.1 of the paper. 5.subjects\ Contains data potentially used in analysis. 6.timelog\ 6.1.timelog\Regulation_timelog.do Contains calculations for Figure 4 and Figure A1 of the paper. 7.z-Tree\ This folder contains the experimental software. 7.1.z-Tree\*.zdata Parameter files for group assignment, market assignment, subject identifiers etc. used in the experimental software. 7.2.z-Tree\z-Tree_Documentation.pdf Documentation of all variables used in the z-Tree experimental software. 8.DetectionProbability.xlsx Contains data for Table 2 in the paper. Based on 4.1 Predictions\Regulation_predictions.do, section stata02. 9.groups.zdata Overview of group IDs. Used In some analyses. 10.Insider_regulation_data.do Stata .do file which processes the raw z-Tree output data (see section 12 of this document) in preparation for all analyses. 11.PowerAnalysis.R Contains all post-hoc power analyses reported in sections 3.1 and 3.2 of the paper. 12.PUN*_C*.xls Original z-Tree output files containing all experimental results. These are processed into Stata format in Insider_regulation_data.do. 13.ResultsWithReferences.pdf The file “ResultsWithReferences.pdf” contains the results section of the paper, including (in blue) references to the analysis scripts described in the present document.
DOI: 10.5281/zenodo.3901014
title: Catch me if you can. Can human observers identify insiders in asset markets?

description: Supplementary material This documentation provides information on the files and folders contained in the supplementary material. Note that all .do and .R files in the supplementary material are meant to be run directly from the folder containing them. 1.contracts\ 1.1.contracts\RegulationContractsModPas.do Contains calculations for the results reported in section 3.2 and for Table 5 of the paper. 2.globals\ Contains data potentially used in analysis. 3.infodata\ Contains data potentially used in analysis. 4.predictions\ 4.1.Predictions\Regulation_predictions.do Contains calculations for Tables 2-4 of the paper. 4.2.Predictions\Regulation_predictions_DetectionProb.do Contains calculations for the results reported in section 3.1 of the paper. 5.subjects\ Contains data potentially used in analysis. 6.timelog\ 6.1.timelog\Regulation_timelog.do Contains calculations for Figure 4 and Figure A1 of the paper. 7.z-Tree\ This folder contains the experimental software. 7.1.z-Tree\*.zdata Parameter files for group assignment, market assignment, subject identifiers etc. used in the experimental software. 7.2.z-Tree\z-Tree_Documentation.pdf Documentation of all variables used in the z-Tree experimental software. 8.DetectionProbability.xlsx Contains data for Table 2 in the paper. Based on 4.1 Predictions\Regulation_predictions.do, section stata02. 9.groups.zdata Overview of group IDs. Used In some analyses. 10.Insider_regulation_data.do Stata .do file which processes the raw z-Tree output data (see section 12 of this document) in preparation for all analyses. 11.PowerAnalysis.R Contains all post-hoc power analyses reported in sections 3.1 and 3.2 of the paper. 12.PUN*_C*.xls Original z-Tree output files containing all experimental results. These are processed into Stata format in Insider_regulation_data.do. 13.ResultsWithReferences.pdf The file “ResultsWithReferences.pdf” contains the results section of the paper, including (in blue) references to the analysis scripts described in the present document.
DOI: 10.5281/zenodo.3901025
title: Observational studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of observational studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5281/zenodo.3901030
title: RCTs and quasi-experimental studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of RCTs and quasi-experimental studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5258/soton/d1427
title: Carbon dioxide (ppm) data sets for thesis 'Sustainability benefits of energy behaviour change in a NHS Trust'

description: Data sets gathered over a nine month period from the carbon dioxide (CO2) monitors located in three older persons' acute care wards of the NHS community hospital participating in the research project entitled 'Sustainability benefits of energy behaviour change in a NHS Trust'.
DOI: 10.7910/dvn/x25yt1/1tntru
title: SDI_computation.py

description: Python codes for computing Social Distancing Index (SDI) scores.
DOI: 10.17632/8mskj36xbj.1
title: Normative Value of Timed Up and Go test in Healthy Adults with Flatfoot: An observational study

description: ABSTRACT Background: In flatfoot, medial longitudinal arch is relaxed or descend downwards or it could be completely lost leading to functional and structural deformity so the arch is not able to absorb impacted forces and reduce the sense of balance which in turn leads to decreased functional stability while walking and running. Purpose:Previous literature shows that dynamic balance is affected in individuals with flat foot and TUG is widely used in clinical practice to assess dynamic balance and functional mobility.The normal values of TUGhave been known in geriatric and paediatric population .In adults, the normative value of TUG is known for individuals with normal foot arch, as best of our knowledge data is missing for subjects with flat foot in healthy adult population, therefore the present study was conducted. Material and Methods:Total sixty participants of age group 18 to 25 years with flatfoot were recruited on the basis of inclusion criteria that includes navicular drop less than 9mm. Demographic data was taken and TUG was performed by standing up out of a chair, walking 3 m, turning around, walking back to the chair, and sitting down. Results:The result findings of the total 60 subjects were expressed in terms of mean and standard deviation as the data follows normal distribution. The present study shows a normative range 6.07-11.53 seconds with mean and standard deviation of 8.23 ±1.15 in age group of 18 to 25 years among asymptomatic adults with flat foot. This study also finds the normative reference value for underweight, Normal and overweight and obese individuals. In underweight group, mean TUG value is 8.19±1.43 with range 6.64-11.53 seconds. In normal weight group, mean TUG value is 8.16±1.05 with range 6.07- 10.55 seconds and in obese and overweight group, mean TUG value is 8.42±1.25 with range of 6.58-10.50 seconds. Conclusion: This study results gave the normative reference value for the healthy adults with flatfoot in age group 18-25 years. The TUG may have utility for primary care providers as they assess and monitor dynamic balance in younger adults with flatfoot, especially according their BMI. Keywords:Flatfoot; Body Mass Index; Foot; Reference values.
DOI: 10.5281/zenodo.3900114
title: The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research

description: We present a database of acoustic transfer functions of the Hearpiece, an openly available multi-microphone multi-driver in-the-ear earpiece for hearing device research. The database includes HRTFs for 87 incidence directions as well as responses of the drivers, all measured at the four microphones of the Hearpiece as well as the eardrum in the occluded and open ear. The transfer functions were measured in both ears of 25 human subjects and a KEMAR with anthropometric ears for five reinsertions of the device. We describe the measurements of the database and analyse derived acoustic parameters of the device. All regarded transfer functions are subject to differences between subjects as well as variations due to reinsertion into the same ear. Also, the results show that KEMAR measurements represent a median human ear well for all assessed transfer functions. The database is a rich basis for development, evaluation and robustness analysis of multiple hearing device algorithms and applications.
description: Funded by the Deutsche Forschungsgemeinschaft (DFG) – Project Nr. 352015383 – SFB 1330 HAPPAA A4.
description: {"references": ["Florian Denk and Birger Kollmeier (2020) The Hearpiece database of individual transfer functions of an openly available in-the-ear earpiece for hearing device research, arXiv:2004.06579"]}
DOI: 10.5281/zenodo.3901083
title: Dataset: Accuracy of Motor Error Predictions for Different Sensory Signals

description: Supplementary Data for <em><strong>Accuracy of Motor Error Predictions for Different Sensory Signals </strong></em>article Dataset associated with the following publication: Joch, M., Hegele, M., Maurer, H., Müller, H., & Maurer, L. K. (2018). Accuracy of Motor Error Predictions for Different Sensory Signals. <em>Frontiers in Psychology</em>, <em>9 </em>(August), 1–13. https://doi.org/10.3389/fpsyg.2018.01376
DOI: 10.5281/zenodo.3901082
title: Dataset: Accuracy of Motor Error Predictions for Different Sensory Signals

description: Supplementary Data for <em><strong>Accuracy of Motor Error Predictions for Different Sensory Signals </strong></em>article Dataset associated with the following publication: Joch, M., Hegele, M., Maurer, H., Müller, H., & Maurer, L. K. (2018). Accuracy of Motor Error Predictions for Different Sensory Signals. <em>Frontiers in Psychology</em>, <em>9 </em>(August), 1–13. https://doi.org/10.3389/fpsyg.2018.01376
DOI: 10.5281/zenodo.3901111
title: Datasets of studying Isoprene oxidation in urban and suburban areas

description: Observational dataset including functionalized isoprene oxidation products, VOCs, trace gases, MET and organic aerosol composition measured at SORPES, Nanjing (118.9E,32.1N) and ECUST, Shanghai (121.5E,30.9N) during summer of 2018. The measurement period is July 13th- August 9th and June 21th-June 26th, respectively. The isoprene functionalized oxidation products were measured by NO3- CI-API-TOF, VOCs were measured by PTR-TOF-MS and ogrganic aerosol composition was measured by TOF-ACSM.
DOI: 10.5281/zenodo.3901110
title: Datasets of studying Isoprene oxidation in urban and suburban areas

description: Observational dataset including functionalized isoprene oxidation products, VOCs, trace gases, MET and organic aerosol composition measured at SORPES, Nanjing (118.9E,32.1N) and ECUST, Shanghai (121.5E,30.9N) during summer of 2018. The measurement period is July 13th- August 9th and June 21th-June 26th, respectively. The isoprene functionalized oxidation products were measured by NO3- CI-API-TOF, VOCs were measured by PTR-TOF-MS and ogrganic aerosol composition was measured by TOF-ACSM.
DOI: 10.5281/zenodo.3901029
title: On calculating the bending modulus of lipid bilayer membranes from buckling simulations

description: Molecular dynamics simulations files that correspond to the buckling simulations method to calculate the bending modulus and the box area fluctuations method to calculate the area compressibility modulus. We performed all molecular dynamics (MD) simulations using the GROMACS software (v. 2016.4) and the MARTINI coarse-grained (CG) force field (v. 2.2), using standard simulation parameters. Each file contains GRO, TOP, ITPs, and MDP files that correspond to the equilibration and the production runs. The name of each folder indicates the types of lipids. We simulate single-component lipid bilayers includes: DLPC, (14:0-14:0), DPPC (16:0-16:0), POPC (16:0-18:1), DOPC (18:1-18:1), PUPC (16:0-22:6), DLiPC (18:2-18:2), DNPC (24:6-24:6), POPG (16:0-18:1), POPS (16:0-18:1), POPE (16:0-18:1), and DPSM (16:0-16:0), and lipid mixtures includes : DOPC:CHOL, DPPC:CHOL, POPC:POPE, DPPC:DLPC, POPC:PUPC, DNPC:DLPC, and DPPC:DLiPC:CHOL, with different molar ratios specified in the folders names.
DOI: 10.17632/xc4r8rjskk.1
title: Comparison between the immediate effect of dynamic and static cupping therapy on shoulder range of motion and pectoral muscle length in upper-crossed syndrome

description: ABSTRACT Background: Upper cross syndrome is a postural ailment which results due to overactive and underactive musculature which leads to altered mechanics of the body, and the surrounding structures also undergo overexertion and secondary ailments appear. Methods: Eighteen Asymptomatic subjects with upper cross syndrome were recruited and randomly assigned into two groups. Patients in the Group A received one session of dynamic cupping over the pectoralis minor and upper trapezius muscle while patient in Group B received static cupping over pectoralis minor and upper trapezius muscle. Assessment is taken at before the treatment. Shoulder and cervical Range of motion (ROM) and pectoralis minor muscle length were compared after the cupping session. Results: A significant change (p&lt;0.05) was found in shoulder and cervical ROM and pectoral muscle length in both the group immediately after a cupping session. No significant group difference was observed between the groups (p&gt;0.05). Conclusion: It was concluded that ROM and Muscle Length can be equally improved in one session using either of Dynamic or Static Cupping therapy.
DOI: 10.5519/qd.yfrw82h2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (213 records)
DOI: 10.5281/zenodo.3901180
title: On calculating the bending modulus of lipid bilayer membranes from buckling simulations

description: Molecular dynamics simulations files that correspond to the buckling simulations method to calculate the bending modulus and the box area fluctuations method to calculate the area compressibility modulus. We performed all molecular dynamics (MD) simulations using the GROMACS software (v. 2016.4) and the MARTINI coarse-grained (CG) force field (v. 2.2), using standard simulation parameters. Each file contains GRO, TOP, ITPs, and MDP files that correspond to the equilibration and the production runs. The name of each folder indicates the types of lipids. We simulate single-component lipid bilayers includes: DLPC, (14:0-14:0), DPPC (16:0-16:0), POPC (16:0-18:1), DOPC (18:1-18:1), PUPC (16:0-22:6), DLiPC (18:2-18:2), DNPC (24:6-24:6), POPG (16:0-18:1), POPS (16:0-18:1), POPE (16:0-18:1), and DPSM (16:0-16:0), and lipid mixtures includes : DOPC:CHOL, DPPC:CHOL, POPC:POPE, DPPC:DLPC, POPC:PUPC, DNPC:DLPC, and DPPC:DLiPC:CHOL, with different molar ratios specified in the folders names.
DOI: 10.5281/zenodo.3901028
title: On calculating the bending modulus of lipid bilayer membranes from buckling simulations

description: Molecular dynamics simulations files that correspond to the buckling simulations method to calculate the bending modulus and the box area fluctuations method to calculate the area compressibility modulus. We performed all molecular dynamics (MD) simulations using the GROMACS software (v. 2016.4) and the MARTINI coarse-grained (CG) force field (v. 2.2), using standard simulation parameters. Each file contains GRO, TOP, ITPs, and MDP files that correspond to the equilibration and the production runs. The name of each folder indicates the types of lipids. We simulate single-component lipid bilayers includes: DLPC, (14:0-14:0), DPPC (16:0-16:0), POPC (16:0-18:1), DOPC (18:1-18:1), PUPC (16:0-22:6), DLiPC (18:2-18:2), DNPC (24:6-24:6), POPG (16:0-18:1), POPS (16:0-18:1), POPE (16:0-18:1), and DPSM (16:0-16:0), and lipid mixtures includes : DOPC:CHOL, DPPC:CHOL, POPC:POPE, DPPC:DLPC, POPC:PUPC, DNPC:DLPC, and DPPC:DLiPC:CHOL, with different molar ratios specified in the folders names.
DOI: 10.18112/openneuro.ds002899.v1.0.0
title: DBS Phantom Recordings
DOI: 10.15161/oar.it/30441
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.25829/idiv.1818-13-3001
title: Global distribution of earthworm diversity (Version 1.1)

description: Dataset for the analysis presented in the manuscript "Global distribution of earthworm diversity", as corrected in the erratum
DOI: 10.5281/zenodo.3901250
title: Number of cases of coronavirus disease (COVID-19) in Ireland

description: Datasets in this publication report the number of diagnoses with coronavirus disease (COVID-19) as reported by the Department of Health in Ireland. This includes new cases diagnosed per day and cumulative cases, hospitalisations, ICU admissions, deaths, number of healthcare workers, number of clusters, gender of cases, age groups of cases, mode of transmission, age groups of those hospitalised, and cases per county. To aid standardisation of age groups and cases per county, the population estimates by age group for 2019 and the actual county population in the 2016 Census from Ireland's Central Statistics Office are also included as separate datasets, to allow expression of cases per million population. These are <em>doh_covid_ie_cases_analysis.csv</em>, where data from Ireland's Health Protection Surveillance Centre is included up to midnight on each included date (currently up to 16-Jun-2020). <em>age_population_cso_2019.csv</em> <em>counties_population_cso_2016.csv</em> <em>age_population_cso_2019.csv </em>has been updated to include separate population estimates for those aged 65-74 years, 75-84 years, and 85 years and over. This is in response to the HSPC releasing case and hospitalisation data for these groups rather than a combined 65 years and over group. <em>counties_population_cso_2016.csv </em>has been updated to remove trailing spaces in the 'county' column. <em>doh_covid_ie_cases_analysis.csv </em>is regularly updated at https://github.com/frankmoriarty/covid_ie/blob/master/doh_covid_ie_cases_analysis.csv