cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12129564.v1
title: The Importance of Conference Proceedings in Research Evaluation: a Methodology Based on Scimago Journal Rank (SJR)

description: Conferences are an essential tool for scientific communication. In disciplines such as Computer Science, over 50% of original research results are published in conference proceedings. In this dataset, there is is a list of conference proceedings, categorized Q1 - Q4 by analogy with SJR journal quartiles. We have analyzed the role of conference proceedings in various disciplines and proposed an alternative approach to research evaluation based on conference proceedings and Scimago Journal Rank (SJR). Comparison of the resulting list in Computer Science with the CORE ranking showed a 62% match, as well as an average rank correlation of the distribution by category.
DOI: 10.6084/m9.figshare.12129507
title: Case report data.rar

description: nuscript for submission in the journal"BMC Infectious disease" and would like guidance on data policies and making data available.The manuscript number is"INFD-D-20-00888R1"The title is"Patient with COVID-19 who has no specific onset symptoms and progresses rapidly to death: a case report".Therefore, I uploaded the relevant data of the research on this excellent shared platform for peer review and reader reading. Considering the global COVID-19 crisis, it is necessary to share relevant research results data to maximize to benefit the global health
DOI: 10.6084/m9.figshare.12130980.v33
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130980.v34
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12129402
title: Index of HiRISE and CTX image numbers

description: Index of HiRISE and CTX image numbers used to produce DEMs of Martian linear dunes.
DOI: 10.6084/m9.figshare.12129303.v1
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel.
DOI: 10.6084/m9.figshare.12129219
title: Hydrochemical and isotopic characteristics of thermal groundwater at Farafra Oasis, Western Desert, Egypt

description: Location of samples.
DOI: 10.6084/m9.figshare.12129303.v2
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.
DOI: 10.6084/m9.figshare.12129219.v1
title: Hydrochemical and isotopic characteristics of thermal groundwater at Farafra Oasis, Western Desert, Egypt

description: Location of samples.
DOI: 10.6084/m9.figshare.12129171
title: Supplementary Material for: In-Hospital Outcomes and Trends of Tricuspid Valve Surgery in Heart Transplant Patients

description: <b><i>Introduction:</i></b> Tricuspid valve (TV) regurgitation is the most common valvular pathology after orthotopic heart transplant (OHT). The number of transplants being performed is increasing with patients living longer after heart transplant. Data on TV surgery in OHT recipients is limited. <b><i>Methods:</i></b> We sought to analyze the outcomes of patients undergoing TV surgery from a large diverse, multicentric, nationwide cohort using the Nationwide Inpatient Sample (NIS) database. <b><i>Results:</i></b>A total of 42,766 TV repair or replacement (bioprosthetic and mechanical) involving adult patients (age ≥18 years) between 2007 and September 2015 were identified. Of these, 366 were performed in patients with OHT. TV repair was the most common surgery performed in both groups (OHT group and native heart group). Compared to the native group, patients with OHT had a significantly higher incidence of cardiogenic shock (20 vs. 11%, <i>p</i> = 0.024), acute kidney injury (AKI) (59 vs. 30%, <i>p</i> &lt; 0.001), and AKI requiring hemodialysis (13 vs. 4%, <i>p</i> &lt; 0.001). Also, the mean length of stay for the index admission was significantly longer in the OHT group (27 vs. 17 days, <i>p</i> = 0.008). The mortality rate was similar between the two groups (7 vs. 8%, <i>p</i> = 0.753). The number of TV surgeries performed in OHT patients from 2007 to 2014 have remained stable (<i>p</i> = 0.803) compared to those in native heart patients which showed a significantly increasing trend (<i>p</i> = 0.019) during the same time period. <b><i>Conclusions:</i></b> TV surgery remains an important treatment modality among the OHT population and carries a similar mortality during index hospitalization as that in native heart patients undergoing TV surgery.
DOI: 10.6084/m9.figshare.12129303.v3
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.
DOI: 10.6084/m9.figshare.12129171.v1
title: Supplementary Material for: In-Hospital Outcomes and Trends of Tricuspid Valve Surgery in Heart Transplant Patients

description: <b><i>Introduction:</i></b> Tricuspid valve (TV) regurgitation is the most common valvular pathology after orthotopic heart transplant (OHT). The number of transplants being performed is increasing with patients living longer after heart transplant. Data on TV surgery in OHT recipients is limited. <b><i>Methods:</i></b> We sought to analyze the outcomes of patients undergoing TV surgery from a large diverse, multicentric, nationwide cohort using the Nationwide Inpatient Sample (NIS) database. <b><i>Results:</i></b>A total of 42,766 TV repair or replacement (bioprosthetic and mechanical) involving adult patients (age ≥18 years) between 2007 and September 2015 were identified. Of these, 366 were performed in patients with OHT. TV repair was the most common surgery performed in both groups (OHT group and native heart group). Compared to the native group, patients with OHT had a significantly higher incidence of cardiogenic shock (20 vs. 11%, <i>p</i> = 0.024), acute kidney injury (AKI) (59 vs. 30%, <i>p</i> &lt; 0.001), and AKI requiring hemodialysis (13 vs. 4%, <i>p</i> &lt; 0.001). Also, the mean length of stay for the index admission was significantly longer in the OHT group (27 vs. 17 days, <i>p</i> = 0.008). The mortality rate was similar between the two groups (7 vs. 8%, <i>p</i> = 0.753). The number of TV surgeries performed in OHT patients from 2007 to 2014 have remained stable (<i>p</i> = 0.803) compared to those in native heart patients which showed a significantly increasing trend (<i>p</i> = 0.019) during the same time period. <b><i>Conclusions:</i></b> TV surgery remains an important treatment modality among the OHT population and carries a similar mortality during index hospitalization as that in native heart patients undergoing TV surgery.
DOI: 10.6084/m9.figshare.12129303.v4
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.
DOI: 10.6084/m9.figshare.12129126
title: Supplementary Material for: Clinical Consequences of Untreated Dental Caries, Individual Characteristics, and Environmental Factors on Self-Reported Oral Health Measures in Adolescents: A Follow-Up Prevalence Study

description: <b><i>Background/Aims:</i></b> This study evaluated the relationships of clinical consequences of untreated dental caries, individual characteristics, and environmental factors on self-reported oral health measures in adolescents. <b><i>Methods:</i></b> A follow-up prevalence study was conducted involving 406 twelve-year-old adolescents selected from public schools in the eastern area of the city of Manaus, Brazil. Baseline data included clinical consequences of untreated caries (PUFA/pufa index), DMFT, sociodemographic characteristics (sex, parental schooling, and family income), psychosocial factors (sense of coherence [SOC-13 scale], oral health beliefs and self-esteem [Rosenberg Self-Esteem Scale]), and social support (SSA questionnaire). Oral health-related quality of life (OHRQoL [CPQ<sub>11–14</sub>]) and self-rated oral health were assessed at the 6-month follow-up. Structural equation modelling was used to explore the relationships between variables according to the Wilson and Cleary model. <b><i>Results:</i></b> The prevalence of PUFA/pufa was 17.8% and mean DMFT was 1.5. The number of teeth with clinical consequences of untreated caries predicted poor self-rated oral health at the 6-month follow-up. Low parental schooling predicted low family income and clinical consequences of untreated dental caries. Psychosocial factors predicted OHRQoL directly and self-rated oral health indirectly. OHRQoL was linked to self-rated oral health. Clinical consequences of untreated dental caries mediated the relationship of parental schooling with OHRQoL and self-rated oral health. OHRQoL mediated the relationship of psychosocial factors and sex with self-rated oral health. Clinical consequences of untreated dental caries was associated with adolescents’ self-rated oral health. Furthermore, the former was an important mediator on the link between low parental education and adolescents’ self-reported oral health measures. <b><i>Conclusions:</i></b> Socioeconomic status, psychosocial factors, and social support were related to OHRQoL and self-rated oral health via direct and indirect pathways.
DOI: 10.6084/m9.figshare.12129303.v5
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.This collection includes: （1）D1-D3 (Table 1) Statistics of domestic production for EEE, vehicle, and wiring & cable in China.xlsx（2）D4 (Table 2) Statistics of three types of product importation and exportation in China.xlsx（3）D5 (Table 3 ) Weights of each product and those data distributions.xlsx（4）D6-D8 Product consumption amount.xlsx（5）D9 Parameters of Weibull lifespan distribution and the regulated maximum lifetime.xlsx（6）D10 (Table 4) Average content of material stock in product waste.xlsx（7）D11 (Table 5) WEEE generation inventory in 2010-2050.xlsx（8）D11 (Table 6) ELV generation inventory in 2010-2050.xlsx（9）D11 (Table 7) Weight amount of three product waste.xlsx（10）D12 (Table 8) Material stock in waste product during 2010-2050.xlsx
DOI: 10.6084/m9.figshare.12129303.v6
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.This collection includes: （1）D1-D3: Statistics of domestic production for EEE, vehicle, and wiring & cable in China（2）D4: Statistics of three types of product importation and exportation in China（3）D5: Weights of each product and those data distributions（4）D6-D8: Product consumption amount in 1990-2050（5）D9: Parameters of Weibull lifespan distribution and the regulated maximum lifetime（6）D10: WEEE generation inventory in 2010-2050（8）D11: ELV generation inventory in 2010-2050（9）D12: WWC weight generation inventory in 2010-2050
DOI: 10.6084/m9.figshare.12129099
title: Supplementary Material for: Preoperative Radiotherapy Leads to Significant Differences in the Plasma Protein Profile of Rectal Cancer Patients

description: <b><i>Introduction:</i></b> Colorectal cancer (CRC) is the third most common cancer worldwide, accounting for 10% of the global cancer burden. Rectal cancer accounts for around 30% of CRC cases, and patients with resectable rectal cancer are often given preoperative radiotherapy (PRT) to reduce the rate of local recurrence. The human plasma proteome is an exceptionally complex proteome and ideal to study due to its ability to reflect the presence of diseases such as cancer and the ease of obtaining blood samples. Previous proteomic studies involving rectal cancer patients have mostly focused on the identification of proteins involved in resistance to radiotherapy. <b><i>Objective:</i></b> The aim of this study was to investigate the overall effects of PRT on plasma protein expression in rectal cancer patients, as there is a lack of such studies. <b><i>Methods:</i></b> Here, we have used mass spectrometry and subsequent statistical analyses to analyze the plasma samples of 30 rectal cancer patients according to PRT status (positive or negative) and tumor stage (II or III). <b><i>Results and Conclusions:</i></b> We discovered 42 proteins whose levels differed significantly between stage II and III rectal cancer patients who did or did not receive PRT. This study shows that PRT, although localized to the pelvis, leads to measurable, tumor stage-specific changes in plasma protein expression. Future studies of plasma proteins should, when relevant, take this into account and be aware of the widespread effects that PRT has on the plasma proteome.
DOI: 10.6084/m9.figshare.12129096
title: Supplementary Materials IJERPH - Dataset

description: <b>Supplementary Materials:</b>Number of correctly deposited tokens for each participant in the Spanish and English groups for Days 1 and 2 separated by block colour. Five tokens of each colour had to be placed in the corresponding container.
DOI: 10.6084/m9.figshare.12129099.v1
title: Supplementary Material for: Preoperative Radiotherapy Leads to Significant Differences in the Plasma Protein Profile of Rectal Cancer Patients

description: <b><i>Introduction:</i></b> Colorectal cancer (CRC) is the third most common cancer worldwide, accounting for 10% of the global cancer burden. Rectal cancer accounts for around 30% of CRC cases, and patients with resectable rectal cancer are often given preoperative radiotherapy (PRT) to reduce the rate of local recurrence. The human plasma proteome is an exceptionally complex proteome and ideal to study due to its ability to reflect the presence of diseases such as cancer and the ease of obtaining blood samples. Previous proteomic studies involving rectal cancer patients have mostly focused on the identification of proteins involved in resistance to radiotherapy. <b><i>Objective:</i></b> The aim of this study was to investigate the overall effects of PRT on plasma protein expression in rectal cancer patients, as there is a lack of such studies. <b><i>Methods:</i></b> Here, we have used mass spectrometry and subsequent statistical analyses to analyze the plasma samples of 30 rectal cancer patients according to PRT status (positive or negative) and tumor stage (II or III). <b><i>Results and Conclusions:</i></b> We discovered 42 proteins whose levels differed significantly between stage II and III rectal cancer patients who did or did not receive PRT. This study shows that PRT, although localized to the pelvis, leads to measurable, tumor stage-specific changes in plasma protein expression. Future studies of plasma proteins should, when relevant, take this into account and be aware of the widespread effects that PRT has on the plasma proteome.
DOI: 10.6084/m9.figshare.12129303.v7
title: Source code for: China's typical product waste estimation 2010-2050

description: In order to ensure the replicability and reproducibility of our dataset, we share here all codes that were developed for producing this dataset excel. The dataset will be updated regularly in the future.This collection includes: （1）D1-D3: Statistics of domestic production for EEE, vehicle, and wiring & cable in China（2）D4: Statistics of three types of product importation and exportation in China（3）D5: Weights of each product and those data distributions（4）D6-D8: Product consumption amount in 1990-2050（5）D9: Parameters of Weibull lifespan distribution and the regulated maximum lifetime（6）D10: WEEE generation inventory in 2010-2050（8）D11: ELV generation inventory in 2010-2050（9）D12: WWC weight generation inventory in 2010-2050
DOI: 10.6084/m9.figshare.12129066
title: Statistics of Access to Materials Posted in the Digital Repository of the Southern Federal University (https://hub.sfedu.ru/repository/)

description: The file consists of 786 014 material access records. Each record consists of the following items: material id(depersonalized), user id (depersonalized), material type name, short material type name, access type, access datetime.Material id and user id are 36-digit alphanumerical identifiers. These identifiers cannot be used to track original data and were created specifically for the purpose of publishing the statistics. When anonymous access is recorded, user id is blank.<br>Material type name (short material type name) can have one of the following values:- "Учебно-методическое пособие" (teaching_aid),- "Учебное пособие" (tutorial),- "Учебник" (textbook),- "Выпускная квалификационная работа" (degree_work),- "Монография" (monograph),- "Студенческая работа" (student_paper),- "Дополнительный материал" (auxiliary_material),- "Диссертация" (dissertation),- "Препринт" (preprint),- "Автореферат" (diss_abstract),- "Патент" (patent),- "Научная статья" (scientific_article),- "Презентация" (presentation).<br>Access type can have one of the following values: - "read" if user accessed material via built-in reader,- "download" if user has downloaded the material,- "get_description" if user has accessed material description page.<br>Access datetime has the following format: YYYY-MM-DD HH:MM:SS.ZZZZZZ<br>Records are delimited with newline characters, record fields - with semicolons (;).
DOI: 10.6084/m9.figshare.12129054
title: Core genome Multi-Locus Sequence Typing schema of Mycoplasma anserisalpingitidis

description: Mycoplasma anserisalpingitidis core genome Multi-Locus Sequence Typing (cgMLST) schema.
DOI: 10.6084/m9.figshare.12129096.v2
title: Supplementary Materials IJERPH - Dataset

description: <b>Supplementary Materials:</b>Number of correctly deposited tokens for each participant in the Spanish and English groups for Days 1 and 2 separated by block colour. Five tokens of each colour had to be placed in the corresponding container.