cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-08.2571310/2173287
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356414/2173288
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571517/2173290
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-11.2375929/2173291
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2022-08.2571624/2173292
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-09.2339144/2173293
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-11.2378498/2173294
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2021-10.2356318/2173295
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-07.2564659/2173296
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571426/2173297
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-09.2337710/2173301
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2022-07.2567418/2173302
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2021-10.2356477/2173303
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-11.2381286/2173304
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2021-11.2378766/2173305
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2022-08.2571518/2173307
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-07.2292503/2173308
title: Data for EMSL Project 51318 from July 2021
DOI: 10.25582/data.2022-09.2609112/2173309
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2022-08.2569574/2173310
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356303/2173311
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571521/2173312
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-08.2571550/2173313
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-09.2343398/2173314
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-11.2381295/2173315
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2022-09.2609130/2173316
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2022-07.2565926/2173317
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571417/2173318
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356257/2173319
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571442/2173322
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356456/2173323
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571710/2173324
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2350100/2173325
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-11.2378641/2173326
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2022-07.2563615/2173327
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2021-10.2363185/2173330
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571335/2173331
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-08.2571488/2173332
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356451/2173334
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571588/2173335
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2355051/2173336
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571494/2173337
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-09.2609143/2173338
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-09.2339231/2173339
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-10.2367354/2173340
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2356331/2173342
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2569790/2173343
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-11.2378658/2173344
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2021-10.2349268/2173345
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-09.2608720/2173350
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-11.2378066/2173351
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2021-09.2336915/2173354
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-10.2369160/2173356
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2344130/2173358
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2367351/2173360
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2369993/2173361
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571569/2173364
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-08.2571410/2173365
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-07.2561670/2173368
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571364/2173369
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-08.2568548/2173372
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356313/2173373
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2357084/2173374
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2362805/2173375
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2349196/2173376
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-07.2567208/2173377
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571451/2173378
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2353649/2173379
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-07.2567684/2173381
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2021-10.2355645/2173382
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2357091/2173383
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2569642/2173385
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2354959/2173386
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2349846/2173387
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2356329/2173388
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2369459/2173389
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-07.2566539/2173390
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2021-10.2349894/2173393
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-07.2567618/2173394
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571454/2173397
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2022-09.2609093/2173398
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-09.2337697/2173399
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2022-07.2566428/2173401
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2021-10.2370753/2173402
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571687/2173403
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-09.2339205/2173404
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-10.2361584/2173405
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2021-10.2356483/2173406
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571471/2173407
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-07.2292129/2173409
title: Data for EMSL Project 51318 from July 2021
DOI: 10.25582/data.2021-11.2378631/2173410
title: Data for EMSL Project 51318 from November 2021
DOI: 10.25582/data.2022-09.2609179/2173411
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-10.2356312/2173414
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2571555/2173417
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2356272/2173420
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-08.2569908/2173421
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-09.2339146/2173422
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2021-10.2369851/2173423
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-09.2609155/2173424
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-09.2339204/2173425
title: Data for EMSL Project 51318 from September 2021
DOI: 10.25582/data.2022-07.2567021/2173426
title: Data for EMSL Project 51318 from July 2022
DOI: 10.25582/data.2022-08.2571474/2173427
title: Data for EMSL Project 51318 from August 2022
DOI: 10.25582/data.2021-10.2357088/2173428
title: Data for EMSL Project 51318 from October 2021
DOI: 10.25582/data.2022-09.2609141/2173429
title: Data for EMSL Project 51318 from September 2022
DOI: 10.25582/data.2021-09.2337695/2173430
title: Data for EMSL Project 51318 from September 2021
DOI: 10.6084/m9.figshare.12480623
title: miRNA raw data.rar

description: Raw data of miRNA processed at different temperatures
DOI: 10.6084/m9.figshare.12480539
title: ShareData.zip

description: Data discussed in the paper and the analysis code<br>
DOI: 10.6084/m9.figshare.12480620
title: feature database for MODNet

description: This file contains the database consisting of most materials from the MaterialsProject with a large set of corresponding ML features.<br>This file should first be unzipped, and the .pkl is then used for the featurization function in MODNet. (https://github.com/ppdebreuck/modnet)
DOI: 10.6084/m9.figshare.12480623.v1
title: miRNA raw data.rar

description: Raw data of miRNA processed at different temperatures
DOI: 10.6084/m9.figshare.12480539.v1
title: ShareData.zip

description: Data discussed in the paper and the analysis code<br>
DOI: 10.6084/m9.figshare.12480479
title: Supplementary Material for: Major Predictive Factors for Progression of Early to Late Age-Related Macular Degeneration

description: <b><i>Introduction:</i></b> We present a prediction model for progression from early/intermediate to advanced age-related macular degeneration (AMD) within 5.9 years. <b><i>Objectives:</i></b> To evaluate the combined role of genetic, nongenetic, and phenotypic risk factors for conversion from early to late AMD over ≥5 years. <b><i>Methods:</i></b> Baseline phenotypic characteristics were evaluated based on color fundus photography, spectral-domain optical coherence tomography, and infrared images. Genotyping for 36 single-nucleotide polymorphisms as well as systemic lipid and complement measurements were performed. Multivariable backward logistic regression resulted in a final prediction model. <b><i>Results and Conclusions:</i></b> During a mean of 5.9 years of follow-up, 22.4% (<i>n</i> = 52) of the patients (<i>n</i> = 232) showed progression to late AMD. The multivariable prediction model included age, <i>CFH</i> variant rs1061170, pigment abnormalities, drusenoid pigment epithelial detachment (DPED), and hyperreflective foci (HRF). The model showed an area under the curve of 0.969 (95% confidence interval 0.948–0.990) and adequate calibration (Hosmer-Lemeshow test, <i>p</i> = 0.797). In addition to advanced age and carrying a <i>CFH</i> variant, pigment abnormalities, DPED, and HRF are relevant imaging biomarkers for conversion to late AMD. In clinical routine, an intensified monitoring of patients with a high-risk phenotypic profile may be suitable for the early detection of conversion to late AMD.
DOI: 10.6084/m9.figshare.12480473
title: Supporting data for the paper "Tropospheric ozone: a long-standing global threat and how to control it?"

description: This data set contains supporting data for the paper "Tropospheric ozone: a long-standing global threat and how to control it?".Geographical coordinates of more than 4,000 stations all over the world, average O<sub>3</sub> mixing ratios, trends (variation rates), time frames for the calculations and data sources are included in the data set.
DOI: 10.6084/m9.figshare.12480473.v1
title: Supporting data for the paper "Tropospheric ozone: a long-standing global threat and how to control it?"

description: This data set contains supporting data for the paper "Tropospheric ozone: a long-standing global threat and how to control it?".Geographical coordinates of more than 4,000 stations all over the world, average O<sub>3</sub> mixing ratios, trends (variation rates), time frames for the calculations and data sources are included in the data set.
DOI: 10.6084/m9.figshare.12480416
title: COVID-19 CT scans

description: chest CT images for COVID-19 patients
DOI: 10.6084/m9.figshare.12480386
title: Metadata record for: A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins

description: This dataset contains key characteristics about the data described in the Data Descriptor A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12480377
title: Correlation between overall survival and the period without hospitalization or outpatient consultation in incurable cancer patients treated with palliative chemotherapy: an analysis of gastric and pancreatic cancer

description: We retrospectively evaluated gastric or pancreatic cancer patients with an Eastern Cooperative Oncology Group performance status of 0~2 (incurable) who were introduced to the Miyagi Cancer Center (Natori, Japan) between May 1, 2014 and December 31, 2018 and died until December 31, 2019. We collected data on sex, age, the spread of disease, the place of death, and OS. The LOH, and OCT were combined for the entire duration of the clinical course and HFS was obtained by subtracting the LOH and OCT from OS. We analyzed the relation between OS, the LOH, OCT and HFS.
DOI: 10.6084/m9.figshare.12480386.v1
title: Metadata record for: A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins

description: This dataset contains key characteristics about the data described in the Data Descriptor A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.12480377.v1
title: Correlation between overall survival and the period without hospitalization or outpatient consultation in incurable cancer patients treated with palliative chemotherapy: an analysis of gastric and pancreatic cancer

description: We retrospectively evaluated gastric or pancreatic cancer patients with an Eastern Cooperative Oncology Group performance status of 0~2 (incurable) who were introduced to the Miyagi Cancer Center (Natori, Japan) between May 1, 2014 and December 31, 2018 and died until December 31, 2019. We collected data on sex, age, the spread of disease, the place of death, and OS. The LOH, and OCT were combined for the entire duration of the clinical course and HFS was obtained by subtracting the LOH and OCT from OS. We analyzed the relation between OS, the LOH, OCT and HFS.
DOI: 10.6084/m9.figshare.12480350
title: Well-being in pandemic_Raw Data_Spasovski_Kenig.sav

description: Data set - responses of participants in a survey on well-being of students during self-isolation due to pandemic.<b>Background:</b> Covid-19 pandemic resulted with a lock-down measure imposed by the government of North Macedonia. Conditions of self-isolation during pandemic affect the mental health. We research the possible protective factors of psychological well-being. <b>Method: </b>A total of 510 college students from the biggest university in the country (70% females, <i>M </i>age = 21.12 years, <i>SD </i>= 1.58) responded to a structured online questionnaire, one month after the country's complete lock down. <b>Results: </b>The correlational analysis suggests that at this age, psychological well-being in conditions of isolation is higher when the perceived social support and adequacy of being informed about the virus, as well the self-engagement with physical activities are higher. Further, respondents who assessed and accept the official medical and restrictive measures higher, reported better overall well-being. Finally, those students who hold conspiratorial beliefs about the virus spread tend to feel more contented than those who do not. <b>Conclusions:</b> In the face of the possible second wave of pandemic, policy creators and scientific community should develop well-thought strategy, tailored to different groups, to support people to cope with pandemic, and to prevent fake news and conspiracy theories which undermine confidence in the health system.
DOI: 10.6084/m9.figshare.12480341
title: Supplementary Material for: <b><i>NT5C2</i></b> Gene Polymorphisms and the Risk of Coronary Heart Disease

description: <b><i>Background:</i></b> Increasing studies have reported that 5′-nucleotidase cytosolic II (<i>NT5C2</i>) has a strong relationship with coronary heart disease (CHD) development. This study was designed to examine the relationship between <i>NT5C2</i> polymorphisms and CHD in the Chinese Han population. <b><i>Methods:</i></b> We studied 501 CHD patients and 496 healthy controls from the Second Affiliated Hospital of Hainan Medical University in Hainan Province, China. Four single nucleotide polymorphisms (SNPs) in <i>NT5C2</i> were selected and genotyped using Agena MassARRAY technology. Odds ratios and 95% confidence intervals were calculated using logistic regression after adjusting for age and gender. Stratification analysis was performed by age and gender in all individuals; we especially investigated the effects of <i>NT5C2</i> SNPs on hypertension and diabetes among CHD patients. <b><i>Results:</i></b> rs2148198 of <i>NT5C2</i> was strongly associated with an increased risk of CHD (allele: <i>p</i> = 0.045; codominant: <i>p</i> = 0.007; additive: <i>p</i> = 0.016). Stratified analysis revealed that rs2148198 was associated with increased CHD risk in individuals aged ≤61 years and males. For CHD patients, rs2148198 significantly affected the risk of hypertension and diabetes (<i>p</i> &lt; 0.05). Further, rs79237883 of <i>NT5C2</i> was associated with decreased susceptibility to hypertension in multiple genetic models for individuals with CHD (allele: <i>p</i> = 0.007; codominant: <i>p</i> = 0.001; dominant: <i>p</i> = 0.001; additive: <i>p</i> = 0.008). <b><i>Conclusion:</i></b> This study reports the association of <i>NT5C2</i> gene variants and CHD susceptibility in the Chinese Han population. Especially, <i>NT5C2</i> rs2148198 was significantly associated with CHD risk in the subgroups of males, hypertension, and diabetes.
DOI: 10.6084/m9.figshare.12480344
title: Chen F et al 2020 QSR-Holocene Tibetan Plateau review-Data

description: Data of a review of Holocene climate change, vegetation history, and landscape responses on the Tibetan Plateau. Stacks of climate change, glacier activity, eolian activity and vegetation change, and pollen-based temperature and precipitation reconstructions for some specific lakes are included. Original reference: Chen F. et al., 2020. Climate change, vegetation history, and landscape responses in the Tibetan Plateau during the Holocene: a comprehensive review. Quaternary Science Reviews. (under review)
DOI: 10.6084/m9.figshare.12480323
title: Well-being in Pandemic_Raw Data

description: Data set - responses of participants in a survey on well-being of students during self-isolation due to pandemic. <b>Background:</b> Covid-19 pandemic resulted with a lock-down measure imposed by the government of North Macedonia. Conditions of self-isolation during pandemic affect the mental health. We research the possible protective factors of psychological well-being. <b>Method: </b>A total of 510 college students from the biggest university in the country (70% females, <i>M </i>age = 21.12 years, <i>SD </i>= 1.58) responded to a structured online questionnaire, one month after the country's complete lock down. <b>Results: </b>The correlational analysis suggests that at this age, psychological well-being in conditions of isolation is higher when the perceived social support and adequacy of being informed about the virus, as well the self-engagement with physical activities are higher. Further, respondents who assessed and accept the official medical and restrictive measures higher, reported better overall well-being. Finally, those students who hold conspiratorial beliefs about the virus spread tend to feel more contented than those who do not. <b>Conclusions:</b> In the face of the possible second wave of pandemic, policy creators and scientific community should develop well-thought strategy, tailored to different groups, to support people to cope with pandemic, and to prevent fake news and conspiracy theories which undermine confidence in the health system.
DOI: 10.6084/m9.figshare.12480323.v1
title: Well-being in Pandemic_Raw Data

description: Data set - responses of participants in a survey on well-being of students during self-isolation due to pandemic. <b>Background:</b> Covid-19 pandemic resulted with a lock-down measure imposed by the government of North Macedonia. Conditions of self-isolation during pandemic affect the mental health. We research the possible protective factors of psychological well-being. <b>Method: </b>A total of 510 college students from the biggest university in the country (70% females, <i>M </i>age = 21.12 years, <i>SD </i>= 1.58) responded to a structured online questionnaire, one month after the country's complete lock down. <b>Results: </b>The correlational analysis suggests that at this age, psychological well-being in conditions of isolation is higher when the perceived social support and adequacy of being informed about the virus, as well the self-engagement with physical activities are higher. Further, respondents who assessed and accept the official medical and restrictive measures higher, reported better overall well-being. Finally, those students who hold conspiratorial beliefs about the virus spread tend to feel more contented than those who do not. <b>Conclusions:</b> In the face of the possible second wave of pandemic, policy creators and scientific community should develop well-thought strategy, tailored to different groups, to support people to cope with pandemic, and to prevent fake news and conspiracy theories which undermine confidence in the health system.