cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13152075
title: Additional file 15 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 15: Supplemental Table 13. Annual amount of pesticides used to produce Total Food Demand, by Alternative Healthy Eating Index-2010 quintile Total Food Demand represents the sum of retail waste, consumer waste, inedible portions, and consumed food. Higher quintiles represent higher diet quality. Pesticides include herbicides, insecticides, and fungicides. AHEI-2010, Health Eating Index-2010. NA, not applicable. 1Test for linear trend across quintiles 1 through 5.
DOI: 10.6084/m9.figshare.13152108
title: Additional file 8 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 8: Supplemental Table 6. Daily per capita Total Food Demand (2005–2016), by Alternative Healthy Eating Index-2010 quintile (n = 50,014) Higher quintiles represent higher diet quality. 1Test for linear trend across quintiles 1 through 5, not adjusted for covariates. 2Test for linear trend across quintiles 1 through 5, adjusted for age (continuous) and sex (male/female). 3Includes vegetable juice. 4Mostly candy, soft drinks, and other beverages.
DOI: 10.6084/m9.figshare.13152108.v1
title: Additional file 8 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 8: Supplemental Table 6. Daily per capita Total Food Demand (2005–2016), by Alternative Healthy Eating Index-2010 quintile (n = 50,014) Higher quintiles represent higher diet quality. 1Test for linear trend across quintiles 1 through 5, not adjusted for covariates. 2Test for linear trend across quintiles 1 through 5, adjusted for age (continuous) and sex (male/female). 3Includes vegetable juice. 4Mostly candy, soft drinks, and other beverages.
DOI: 10.6084/m9.figshare.13152102.v1
title: Additional file 6 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 6: Supplemental Table 4. Alternative Healthy Eating Index-2010 component scores, National Health and Nutrition Examination Survey, 2005–2016 (n = 50,014) EPA, eicosapentaenoic acid (20:5, n-3). DHA, docosahexaenoic acid (22:6, n-3). Includes individuals ≥2 years of age.
DOI: 10.6084/m9.figshare.13152099.v1
title: Additional file 5 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 5: Supplemental Table 3. Healthy Eating Index-2015 component scores, National Health and Nutrition Examination Survey, 2005–2016 (n = 50,014) Includes individuals ≥2 years of age.
DOI: 10.6084/m9.figshare.13152105.v1
title: Additional file 7 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 7: Supplemental Table 5. Daily per capita Total Food Demand (2005–2016), by Healthy Eating Index-2015 quintile (n = 50,014) Higher quintiles represent higher diet quality. 1Test for linear trend across quintiles 1 through 5, not adjusted for covariates. 2Test for linear trend across quintiles 1 through 5, adjusted for age (continuous) and sex (male/female). 3Includes vegetable juice. 4Mostly candy, soft drinks, and other beverages.
DOI: 10.6084/m9.figshare.13152111.v1
title: Additional file 9 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 9: Supplemental Table 7. Annual amount of agricultural land used to produce Total Food Demand, by land use category and Healthy Eating Index-2015 quintile Total Food Demand represents the sum of retail waste, consumer waste, inedible portions, and consumed food. Higher quintiles represent higher diet quality. HEI-2015, Healthy Eating Index-2015. 1Test for linear trend across quintiles 1 through 5.
DOI: 10.6084/m9.figshare.13152102
title: Additional file 6 of Healthy diets can create environmental trade-offs, depending on how diet quality is measured

description: Additional file 6: Supplemental Table 4. Alternative Healthy Eating Index-2010 component scores, National Health and Nutrition Examination Survey, 2005–2016 (n = 50,014) EPA, eicosapentaenoic acid (20:5, n-3). DHA, docosahexaenoic acid (22:6, n-3). Includes individuals ≥2 years of age.
DOI: 10.6084/m9.figshare.13152151
title: Additional file 1 of Chemoinformatics-based enumeration of chemical libraries: a tutorial

description: Additional file 1. This document describes the substructure search in the ZINC database; the filter parameters for Congreve’s Rule of 3 used in the FAF-Drugs server; the instructions for filtering substructures in Data Warrior and Figure S1.
DOI: 10.6084/m9.figshare.13152151.v1
title: Additional file 1 of Chemoinformatics-based enumeration of chemical libraries: a tutorial

description: Additional file 1. This document describes the substructure search in the ZINC database; the filter parameters for Congreve’s Rule of 3 used in the FAF-Drugs server; the instructions for filtering substructures in Data Warrior and Figure S1.
DOI: 10.6084/m9.figshare.13152183
title: Additional file 1 of Effects of toner-handling work on respiratory function, chest X-ray findings, and biomarkers of inflammation, allergy, and oxidative stress: a 10-year prospective Japanese cohort study

description: Additional file 1. Comparison of discontinued biomarkers between the toner-handling group and non-toner-handling groups.
DOI: 10.6084/m9.figshare.13152183.v1
title: Additional file 1 of Effects of toner-handling work on respiratory function, chest X-ray findings, and biomarkers of inflammation, allergy, and oxidative stress: a 10-year prospective Japanese cohort study

description: Additional file 1. Comparison of discontinued biomarkers between the toner-handling group and non-toner-handling groups.
DOI: 10.6084/m9.figshare.13152246
title: Additional file 2 of Molecular detection of drug resistant polymorphisms in Plasmodium falciparum isolates from Southwest, Nigeria

description: Additional file 2. Allelic discrimination.
DOI: 10.6084/m9.figshare.13152246.v1
title: Additional file 2 of Molecular detection of drug resistant polymorphisms in Plasmodium falciparum isolates from Southwest, Nigeria

description: Additional file 2. Allelic discrimination.
DOI: 10.14457/mu.res.2018.50
title: โครงการ การประชุมเชิงปฏิบัติการ สถานการณ์การย้ายถิ่นข้ามชาติและองค์ความรู้ด้านการบริหารจัดการ และการคุ้มกครองดูแลแรงงานข้ามชาติ
DOI: 10.14457/mu.res.2018.51
title: ปริมาณ องค์ประกอบ และมูลค่า รวมถึงแนวทางที่เหมาะสม สำหรับการลดและใช้ประโยชน์ขยะอาหาร จากภาคธุรกิจโรงแรมในเขตกรุงเทพฯ และปริมณฑล
DOI: 10.6084/m9.figshare.13152387
title: Additional file 1 of How does participation in a voluntary prize exam affect medical students’ knowledge and interest in ENT, plastic surgery, ophthalmology and dermatology?

description: Additional file 1. Student Responses. Knowledge and interest scores pre and post exam. Data analysis.
DOI: 10.6084/m9.figshare.13152387.v1
title: Additional file 1 of How does participation in a voluntary prize exam affect medical students’ knowledge and interest in ENT, plastic surgery, ophthalmology and dermatology?

description: Additional file 1. Student Responses. Knowledge and interest scores pre and post exam. Data analysis.
DOI: 10.14457/mu.res.2019.6
title: พหุวัฒนธรรมอาเซียนในพื้นที่โลกออฟไลน์และออนไลน์ : พลวัต การบริหารจัดการและสมรรถนะทางพหุวัฒนธรรมในบริบทการย้ายถิ่นข้ามชาติ กรณีศึกษาประเทศไทย สิงคโปร์ และมาเลเซีย
DOI: 10.14457/mu.res.2019.7
title: การจัดการความหลากหลายทางด้านภาษา ในสังคมพหุวัฒนธรรมอาเซียน : กรณีศึกษากลุ่มแรงงานข้ามชาติไร้ฝีมือ ในประเทศไทย สิงคโปร์ และมาเลเซีย
DOI: 10.14457/mu.res.2019.8
title: การศึกษาเปรียบเทียบนโยบายและการปฏิบัติการของรัฐภายใต้กรอบอาเซียน : ศึกษาผ่านกลุ่มผู้ลี้ภัยและผู้แสวงหาที่ลี้ภัยในเมือง ในประเทศอินโดนีเซีย มาเลเซีย และไทย
DOI: 10.14457/mu.res.2019.9
title: พื้นที่สุขภาพข้ามพรมแดน : พลวัตการใช้บริการสุขภาพ ระบบสุขภาพ และความทุกข์ทางสังคมของคนข้ามแดน
DOI: 10.14457/mu.res.2019.10
title: การศึกษาระบบการตรวจสอบและรับรองสินค้าอาหารกับการค้าระหว่างประเทศ ภายใต้กฎระเบียบใหม่
DOI: 10.14457/mu.res.2020.1
title: โครงการนวัตกรรมทางเทคโนโลยีชีวภาพสำหรับผลิตภัณฑ์ เสริมอาหาร เพื่อการส่งเสริมสุขภาพที่ยั่งยืน
DOI: 10.14457/mu.res.2020.2
title: โครงการ ศูนย์พี่เลี้ยงโครงการเพาะพันธุ์ปัญญา ศูนย์พี่เลี้ยงสถาบันนวัตกรรมการเรียนรู้ มหาวิทยาลัยมหิดล
DOI: 10.14457/mu.res.2020.3
title: แผนงานส่งเสริมและสนับสนุนการวิจัยที่มุ่งเป้าตอบสนอง ความต้องการในการพัฒนาประเทศ ประจำปีงบประมาณ 2561: กลุ่มเรื่องประชาคมอาเซียน
DOI: 10.14457/mu.res.2020.4
title: การส่งพลังงานแบบไร้สายสำหรับการประจุพลังงานยานยนต์ไฟฟ้า (ระยะที่ 1)
DOI: 10.14457/mu.res.2020.5
title: การย้ายถิ่นของมุสลิมไทยไปมาเลเซีย และการบูรณาการทางสังคม กับการแก้ไขปัญหาในสามจังหวัดชายแดนภาคใต้
DOI: 10.14457/mu.res.2018.52
title: โครงการทบทวนสถานภาพการประยุกต์ใช้เทคโนโลยีบล็อกเชนและสกุลเงินดิจิทัล สำหรับระบบสาธารณูปโภคไฟฟ้า
DOI: 10.14457/mu.res.2018.53
title: โครงการการศึกษาแนวทางเพิ่มประสิทธิภาพการพัฒนาเมืองสู่ความยั่งยืนผ่านกระบวนการมีส่วนร่วม กรณีศึกษาเมืองศาลายา จังหวัดนครปฐม
DOI: 10.5281/zenodo.4146105
title: Dataset for 2020GL089882

description: This archive includes data used in the manuscript of 2020GL089882. 1. Kii2015data1.tar: Wave form data (Win format,2015/5-2015/9) and chtable file 2. Kii2015data2.tar: Wave form data (Win format,2015/10-2015/12) and chtable file 3. Kii2015-stationALL.dat: Station correction data 4. Kii2015-Vp.xyz: Tomography-derived P-wave velocity model (X,Y,Z,Vp) 5. Kii2015-Vs.xyz: Tomography-derived S-wave velocity model (X,Y,Z,Vs) 6. Kii2015-refimage.grd: Reflectivity model (netCDF grid data) 7. Kii2015-EQ.dat: Relocated EQ hypocenters using our arrival time picks 8. Kii2015-EQ2.dat: Relocated EQ hypocenters using the JMA arrival time picks 9. Kii2015-LFE.dat: Relocated LFE hypocenters using our arrival time picks 10. Kii2015-pick.cnv: Earthquake /LFE pick
DOI: 10.5281/zenodo.4146104
title: Dataset for 2020GL089882

description: This archive includes data used in the manuscript of 2020GL089882. 1. Kii2015data1.tar: Wave form data (Win format,2015/5-2015/9) and chtable file 2. Kii2015data2.tar: Wave form data (Win format,2015/10-2015/12) and chtable file 3. Kii2015-stationALL.dat: Station correction data 4. Kii2015-Vp.xyz: Tomography-derived P-wave velocity model (X,Y,Z,Vp) 5. Kii2015-Vs.xyz: Tomography-derived S-wave velocity model (X,Y,Z,Vs) 6. Kii2015-refimage.grd: Reflectivity model (netCDF grid data) 7. Kii2015-EQ.dat: Relocated EQ hypocenters using our arrival time picks 8. Kii2015-EQ2.dat: Relocated EQ hypocenters using the JMA arrival time picks 9. Kii2015-LFE.dat: Relocated LFE hypocenters using our arrival time picks 10. Kii2015-pick.cnv: Earthquake /LFE pick
DOI: 10.6084/m9.figshare.10012559
title: A dataset of distributed global water withdrawal from 1960 to 2017

description: Global water withdrawal data
DOI: 10.6084/m9.figshare.8063150
title: A data set of distributed global population and water withdrawal from 1960 to 2017

description: global population and water withdrawal
DOI: 10.6084/m9.figshare.8063150.v3
title: A data set of distributed global population and water withdrawal from 1960 to 2017

description: global population and water withdrawal
DOI: 10.5281/zenodo.4145924
title: Regionally aggregated, stitched and de-drifted CMIP-climate data, processed with netCDF-SCM v2.0.0

description: The world’s most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model<br> Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the<br> total data volume will be in the order of 20PB. Here we present a dataset of annual, monthly, global-, hemispheric- and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing simple climate models against the behaviour of physically complete models. In addition to its use for simple climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global-, hemispheric- and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialised ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, i.e. identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way.
DOI: 10.5281/zenodo.4146486
title: Regionally aggregated, stitched and de-drifted CMIP-climate data, processed with netCDF-SCM v2.0.0

description: The world’s most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model<br> Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the<br> total data volume will be in the order of 20PB. Here we present a dataset of annual, monthly, global-, hemispheric- and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing simple climate models against the behaviour of physically complete models. In addition to its use for simple climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global-, hemispheric- and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialised ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, i.e. identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way.
DOI: 10.6084/m9.figshare.13152848
title: sub-01_ses-01_dwi_space-RASMM_model-DTI_desc-det-AFQ-clean_tractography.trk

description: This is a tractography derived from the Stanford HARDI dataset (https://purl.stanford.edu/ng782rw8378)<br>
DOI: 10.6084/m9.figshare.13152848.v1
title: sub-01_ses-01_dwi_space-RASMM_model-DTI_desc-det-AFQ-clean_tractography.trk

description: This is a tractography derived from the Stanford HARDI dataset (https://purl.stanford.edu/ng782rw8378)<br>
DOI: 10.17632/6h6gv38xy8.1
title: Data for: Effects of h-BN particles on the structure, oxidation behaviour, and kinetics of C/C composites fabricated via CVI

description: The isothermal oxidation data of C/C and C/C-BN composites is shown in the document at 600-900℃
DOI: 10.21227/8kj3-f984
title: Model1

description: We have followed the three-step approach to tackle this challenge.Data preparation – We started with aggregation of the energy consumption data at daily level (originally given at a half hourly interval). Missing values were observed in energy consumption series and temperature metadata, which we imputed using daily level linear interpolation.Next was feature engineering which was essential to being out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data – used as is (we have used avg. weather data only)b) Time Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon,Tue,……Sun)c) Cyclic encoding of periodic features for month and weekday1. Month gets mapped to Month_x = sin(2*pi*month/12) and Month_y=cos(2*pi*month/12)2. Weekday gets mapped to weekday_x = sin(2*pi*weekday/6) and weekday_y=cos(2*pi*weekday /6).Post multiple iterations, we settled with the XgBoost model since it performed the best among all. We trained the model on individual meter_ID and forecasted energy consumption at a daily level. The outcome was then rolled-up, to monthly level to get our final submission.
DOI: 10.13012/b2idb-4623305_v1
title: Articles With PubMed Identifiers

description: The data file contains a list of articles that have PubMed identifiers, which were used in a project associated with the manuscript "An in-situ evaluation of the RCT Tagger using 7413 articles included in 570 Cochrane reviews with RCT-only inclusion criteria".
DOI: 10.18170/dvn/1zigvs
title: Attitudes towards Citizenship in China-2008

description: Attitudes towards Citizenship in China-2008
DOI: 10.34740/kaggle/dsv/1598785
title: COVID-19 cases by Continent
DOI: 10.21227/t2gh-z590
title: Ear-EEG Recording for Brain Computer Interface of Motor Task

description: Ear-EEG recording collects brain signals from electrodes placed in the ear canal. Compared with existing scalp-EEG, ear-EEG is more wearable and user-comfortable compared with existing scalp-EEG.In this dataset, we collected ear-EEG signals and the scalp-EEG signals when subjects were performing a left/right hand grasping motor task. We first validated the ear-EEG recordings by measuring the similarity of power ratio and channel correlation between ear-EEG and scalp-EEG signals. Then we applied EEG compact network (EEGNet) for classification of left/right-hand motor tasks using ear-EEG signals and scalp EEG signals separately. Our results showed motor task classification based on ear-EEG has a high potential for the practical BCI applications in motor task.
DOI: 10.6084/m9.figshare.13152872
title: Supplementary Material for: How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery

description: <b><i>Introduction:</i></b> A surgeon’s technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. <b><i>Methods:</i></b> We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. <b><i>Results:</i></b> We determine that for the majority of skills, only a small subset of OPIs (2–10) are required to achieve the highest model accuracies (80–95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80–98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. <b><i>Conclusion:</i></b> We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.
DOI: 10.6084/m9.figshare.13152872.v1
title: Supplementary Material for: How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery

description: <b><i>Introduction:</i></b> A surgeon’s technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. <b><i>Methods:</i></b> We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. <b><i>Results:</i></b> We determine that for the majority of skills, only a small subset of OPIs (2–10) are required to achieve the highest model accuracies (80–95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80–98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. <b><i>Conclusion:</i></b> We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.
DOI: 10.18170/dvn/tmc4pw
title: Institutionalization of Legal Reforms in China-2003

description: Institutionalization of Legal Reforms in China-2003
DOI: 10.18170/dvn/bparrt
title: Public Awareness on Environmental Protection in China-1998

description: Public Awareness on Environmental Protection in China-1998