cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2019.669
title: Comparison of falls risk factors between Thai middle and old age

description: The aims of this study were to compare falls risk factors between middle-aged and old age groups, and to examine the correlations between falls risk factors and falls in each age group. A total of 104 participants from the community, whichconsists of 52 participants in each age group participated in this study. All participants were assessed general health data questionnaires, history of falls, cognitive performance, physical activity level, physical performance (muscle strength, proprioception, balance, flexibility), fear of falling, and environmental fall risk factors. This study found that middle-aged and old age groups reported high history of falls in the previous year. Most of the participants in the middle-aged group were still working and high education level than old age groups. All variables of fall risk factors in middle-aged group were no significant differences between fallers and non-fallers. While physical performance (proprioception, balance) and environmental fall risk factor variables were significant differences between fallers and non-fallers in old age group. Comparison of fall risk factors and number of falls in the middle-aged group found that only flexibility (passive ankle dorsiflexion at left side) was significant correlation. While in old age group found that physical performance and environmental fall risk factors correlated with falls (history of falls, number of falls). In conclusion, fall risk factors between middle-aged and old age groups were not the same factors. In the middle-aged group, the fall risk factors could not be able to clearly define. While old age group clearly showed common risk factors, which associated with falling including physical performance (proprioception and balance: limit of stability, functional mobility) and environmental fall risk factors.
DOI: 10.5281/zenodo.3970888
title: mamenoud/CH4-Lutjewad-2016-2017: Final release

description: Data associated with the article: Menoud, M., van der Veen, C., Scheeren, B., Chen, H., Szénási, B., Morales, R.P., Pison, I., Bousquet, P., Brunner, D., Röckmann, T., 2020. Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements. Tellus B: Chemical and Physical Meteorology 72, 1–19. https://doi.org/10.1080/16000889.2020.1823733
DOI: 10.5281/zenodo.3970887
title: mamenoud/CH4-Lutjewad-2016-2017: Final release

description: Data associated with the article: Menoud, M., van der Veen, C., Scheeren, B., Chen, H., Szénási, B., Morales, R.P., Pison, I., Bousquet, P., Brunner, D., Röckmann, T., 2020. Characterisation of methane sources in Lutjewad, The Netherlands, using quasi-continuous isotopic composition measurements. Tellus B: Chemical and Physical Meteorology 72, 1–19. https://doi.org/10.1080/16000889.2020.1823733
DOI: 10.15468/jyxfcd
title: New data on Garra makiensis (Cyprinidae, Labeoinae) from the Awash River (Ethiopia) with remarks on its relationships to congeners on the Arabian Peninsula

description: This dataset contains the digitized treatments in Plazi based on the original journal article Englmaier, Gernot K., Rodriguez, Nuria Vinuela, Waidbacher, Herwig, Palandacic, Anja, Tesfaye, Genanaw, Gessl, Wolfgang, Meulenbroek, Paul (2020): New data on Garra makiensis (Cyprinidae, Labeoinae) from the Awash River (Ethiopia) with remarks on its relationships to congeners on the Arabian Peninsula. ZooKeys 984: 133-163, DOI: http://dx.doi.org/10.3897/zookeys.984.55982, URL: http://dx.doi.org/10.3897/zookeys.984.55982
DOI: 10.15468/c2upey
title: New stiletto flies from New Caledonia (Therevidae, Agapophytinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Irwin, Michael E., Winterton, Shaun L., Metz, Mark A. (2020): New stiletto flies from New Caledonia (Therevidae, Agapophytinae). ZooKeys 984: 83-132, DOI: http://dx.doi.org/10.3897/zookeys.984.53587, URL: http://dx.doi.org/10.3897/zookeys.984.53587
DOI: 10.15468/6gtkzt
title: A new genus and species of Nannopodidae (Crustacea, Copepoda, Harpacticoida) from the Yellow Sea of South Korea

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kim, Jong Guk, Lee, Jimin (2020): A new genus and species of Nannopodidae (Crustacea, Copepoda, Harpacticoida) from the Yellow Sea of South Korea. ZooKeys 984: 23-47, DOI: http://dx.doi.org/10.3897/zookeys.984.52252, URL: http://dx.doi.org/10.3897/zookeys.984.52252
DOI: 10.15468/e6j8gp
title: New record of Nausithoe werneri (Scyphozoa, Coronatae, Nausithoidae) from the Brazilian coast and a new synonymy for Nausithoe maculata

description: This dataset contains the digitized treatments in Plazi based on the original journal article Molinari, Clarissa Garbi, Maronna, Maximiliano Manuel, Morandini, Andre Carrara (2020): New record of Nausithoe werneri (Scyphozoa, Coronatae, Nausithoidae) from the Brazilian coast and a new synonymy for Nausithoe maculata. ZooKeys 984: 1-21, DOI: http://dx.doi.org/10.3897/zookeys.984.56380, URL: http://dx.doi.org/10.3897/zookeys.984.56380
DOI: 10.15468/7qrv3x
title: Bombus (Pyrobombus) johanseni Sladen, 1919, a valid North American bumble bee species, with a new synonymy and comparisons to other " red-banded " bumble bee species in North America (Hymenoptera, Apidae, Bombini)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sheffield, Cory S., Oram, Ryan, Heron, Jennifer M. (2020): Bombus (Pyrobombus) johanseni Sladen, 1919, a valid North American bumble bee species, with a new synonymy and comparisons to other " red-banded " bumble bee species in North America (Hymenoptera, Apidae, Bombini). ZooKeys 984: 59-81, DOI: http://dx.doi.org/10.3897/zookeys.984.55816, URL: http://dx.doi.org/10.3897/zookeys.984.55816
DOI: 10.15468/mxdzww
title: A new wood-inhabiting mite species of the genus Dendroseius Karg, 1965 (Acari, Mesostigmata, Rhodacaridae) from Central Europe (Slovakia)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Masan, Peter (2020): A new wood-inhabiting mite species of the genus Dendroseius Karg, 1965 (Acari, Mesostigmata, Rhodacaridae) from Central Europe (Slovakia). ZooKeys 984: 49-57, DOI: http://dx.doi.org/10.3897/zookeys.984.57256, URL: http://dx.doi.org/10.3897/zookeys.984.57256
DOI: 10.6084/m9.figshare.13193537.v1
title: DATA

description: Data consisitng of LP, GRHR, RMSSD, GRPD at tunnel entrance and exit<br>
DOI: 10.6084/m9.figshare.13193537
title: DATA

description: Data consisitng of LP, GRHR, RMSSD, GRPD at tunnel entrance and exit<br>
DOI: 10.21227/s0bq-7p64
title: Model12

description: Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in consumption and temperature metadata, which we imputed using daily level linear interpolation.Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data, Three lag variable for average weather data (day1, day2, day3 lag)b) Time-Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the 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 a monthly level to get our final submission.Update- we identified those customers where our model was not performing well and used the Moving average to forecast for these customers.
DOI: 10.5281/zenodo.4246069
title: Datensatz zum Beitrag "Integration von Drohnen in betriebswirtschaftliche Prozesse am Beispiel der Logistik"

description: Der Datensatz enthält das Literatursample zum Beitrag "Integration von Drohnen in betriebswirtschaftliche Prozesse am Beispiel der Logistik" sowie die zugehörige Klassifikation der Arbeiten.
DOI: 10.5281/zenodo.4246068
title: Datensatz zum Beitrag "Integration von Drohnen in betriebswirtschaftliche Prozesse am Beispiel der Logistik"

description: Der Datensatz enthält das Literatursample zum Beitrag "Integration von Drohnen in betriebswirtschaftliche Prozesse am Beispiel der Logistik" sowie die zugehörige Klassifikation der Arbeiten.
DOI: 10.5281/zenodo.4246082
title: Air quality during COVID-19 in Beijing_data_sharing

description: Data for air quality during COVID-19 in Beijing.
DOI: 10.5281/zenodo.4246081
title: Air quality during COVID-19 in Beijing_data_sharing

description: Data for air quality during COVID-19 in Beijing.
DOI: 10.5281/zenodo.4246084
title: The new Garba Guracha palynological sequence: revision and data expansion

description: Dataset by Graciela Gil-Romera et al. submitted to "Palaeocology of Africa". dictionary.csv is the file containing information on how taxa are attibuted to different functional and bioclimatic types and pollen.csv contains all raw pollen data. Charcoal data presented in Fig3 can be downloaded from here: https://github.com/ggilromera/BaleFire The age-depth model has been built using the dates and code published here: https://link.springer.com/article/10.1007/s10933-020-00138-w#Sec36 And the raw and calibrated dated samples can be found in the supmat_bittner_etal_2020.docx file.
DOI: 10.21227/ycj8-2710
title: Dyn baseline

description: Dyn baseline
DOI: 10.6084/m9.figshare.13182404.v1
title: Long-term dynamics of a cladoceran community from an early stage of lake formation in Lake Fukami-ike, Japan

description: The dataset of flux of cladoceran subfossills, fossil pigment and TP. "Sample" means ID of lake sediment core samples. The method to calculate the flux is detailed in the paper.
DOI: 10.6084/m9.figshare.13182404
title: Long-term dynamics of a cladoceran community from an early stage of lake formation in Lake Fukami-ike, Japan

description: The dataset of flux of cladoceran subfossils, fossil pigments and Total phosphorus. "Sample" column represents ID of lake sediment core samples. The method to calculate the flux can be found in the manuscript.
DOI: 10.6084/m9.figshare.13182404.v3
title: Long-term dynamics of a cladoceran community from an early stage of lake formation in Lake Fukami-ike, Japan

description: The dataset of flux of cladoceran subfossils, fossil pigments and Total phosphorus. "Sample" column represents ID of lake sediment core samples. The method to calculate the flux can be found in the manuscript.
DOI: 10.6084/m9.figshare.13182404.v2
title: Long-term dynamics of a cladoceran community from an early stage of lake formation in Lake Fukami-ike, Japan

description: The dataset of flux of cladoceran subfossils, fossil pigments and Total phosphorus. "Sample" column represents ID of lake sediment core samples. The method to calculate the flux can be found in the manuscript.
DOI: 10.21942/uva.13193609
title: Amsterdam Colloquium 2019 experiment data and scripts

description: Data and R scripts from experiments reported in Amsterdam Colloquium 2019 paper, "Negation and Alternatives in conditional Antecedents".
DOI: 10.21942/uva.13193609.v1
title: Amsterdam Colloquium 2019 experiment data and scripts

description: Data and R scripts from experiments reported in Amsterdam Colloquium 2019 paper, "Negation and Alternatives in conditional Antecedents".
DOI: 10.5281/zenodo.4246204
title: The new Garba Guracha palynological sequence: revision and data expansion

description: Dataset by Graciela Gil-Romera et al. submitted to "Palaeocology of Africa". dictionary.csv is the file containing information on how taxa are attibuted to different functional and bioclimatic types and pollen.csv contains all raw pollen data. Charcoal data presented in Fig3 can be downloaded from here: https://github.com/ggilromera/BaleFire The age-depth model has been built using the dates and code published here: https://link.springer.com/article/10.1007/s10933-020-00138-w#Sec36 And the raw and calibrated dated samples can be found in the supmat_bittner_etal_2020.docx file.
DOI: 10.5281/zenodo.4246083
title: The new Garba Guracha palynological sequence: revision and data expansion

description: Dataset by Graciela Gil-Romera et al. submitted to "Palaeocology of Africa". dictionary.csv is the file containing information on how taxa are attibuted to different functional and bioclimatic types and pollen.csv contains all raw pollen data. Charcoal data presented in Fig3 can be downloaded from here: https://github.com/ggilromera/BaleFire The age-depth model has been built using the dates and code published here: https://link.springer.com/article/10.1007/s10933-020-00138-w#Sec36 And the raw and calibrated dated samples can be found in the supmat_bittner_etal_2020.docx file.
DOI: 10.6084/m9.figshare.12520082.v1
title: Supplementary material 1: vouchers molecular specimens

description: Vouchers of specimens used for molecular investigations.
DOI: 10.6084/m9.figshare.12520082
title: Supplementary material 1: vouchers molecular specimens

description: Vouchers of specimens used for molecular investigations.
DOI: 10.6084/m9.figshare.12520079
title: Supplementary material 2: vouchers SEM specimens

description: Vouchers of specimens used for scanning electron microscopy investigations.<br>
DOI: 10.6084/m9.figshare.12520079.v1
title: Supplementary material 2: vouchers SEM specimens

description: Vouchers of specimens used for scanning electron microscopy investigations.<br>
DOI: 10.6084/m9.figshare.12520031.v1
title: Supplementary material 4: alignment files

description: Multiple sequence alignment files of the 16S rRNA, 18S rRNA, 28S rRNA and COI sequences.<br>
DOI: 10.6084/m9.figshare.12520031
title: Supplementary material 4: alignment files

description: Multiple sequence alignment files of the 16S rRNA, 18S rRNA, 28S rRNA and COI sequences.<br>
DOI: 10.6084/m9.figshare.13193624
title: Supplementary Material for: Post-COVID-19 Epidemic: Allostatic Load among Medical and Nonmedical Workers in China

description: <b><i>Background:</i></b> As the fight against the COVID-19 epidemic continues, medical workers may have allostatic load. <b><i>Objective:</i></b> During the reopening of society, medical and nonmedical workers were compared in terms of allostatic load. <b><i>Methods:</i></b> An online study was performed; 3,590 Chinese subjects were analyzed. Socio-demographic variables, allostatic load, stress, abnormal illness behavior, global well-being, mental status, and social support were assessed. <b><i>Results:</i></b> There was no difference in allostatic load in medical workers compared to nonmedical workers (15.8 vs. 17.8%; <i>p</i> = 0.22). Multivariate conditional logistic regression revealed that anxiety (OR = 1.24; 95% CI 1.18–1.31; <i>p</i> &lt; 0.01), depression (OR = 1.23; 95% CI 1.17–1.29; <i>p</i> &lt; 0.01), somatization (OR = 1.20; 95% CI 1.14–1.25; <i>p</i> &lt; 0.01), hostility (OR = 1.24; 95% CI 1.18–1.30; <i>p</i> &lt; 0.01), and abnormal illness behavior (OR = 1.49; 95% CI 1.34–1.66; <i>p</i> &lt; 0.01) were positively associated with allostatic load, while objective support (OR = 0.84; 95% CI 0.78–0.89; <i>p</i> &lt; 0.01), subjective support (OR = 0.84; 95% CI 0.80–0.88; <i>p</i> &lt; 0.01), utilization of support (OR = 0.80; 95% CI 0.72–0.88; <i>p</i> &lt; 0.01), social support (OR = 0.90; 95% CI 0.87–0.93; <i>p</i> &lt; 0.01), and global well-being (OR = 0.30; 95% CI 0.22–0.41; <i>p</i> &lt; 0.01) were negatively associated. <b><i>Conclusions:</i></b> In the post-COVID-19 epidemic time, medical and nonmedical workers had similar allostatic load. Psychological distress and abnormal illness behavior were risk factors for it, while social support could relieve it.
DOI: 10.6084/m9.figshare.12520034
title: Supplementary material 5: maximum likelihood trees and in-/output files (molecular data)

description: Resulting maximum likelihood trees as TREE-files together with the corresponding input-/output-files from the analyses using molecular data.
DOI: 10.6084/m9.figshare.12520037.v1
title: Supplementary material 6: genetic distance calculations

description: Pairwise, within-group and between-group mean distances were estimated for the included taxa and are provided as Excel-file. Groups were defined as follow: (i) subgenus <i>Lithobius</i>, (ii) subgenus <i>Monotarsobius</i>, (iii) subgenus <i>Sigibius</i>, (iv) Lithobiidae, non-<i>Lithobius</i>, and (v) Henicopidae.<br>
DOI: 10.6084/m9.figshare.12520037
title: Supplementary material 6: genetic distance calculations

description: Pairwise, within-group and between-group mean distances were estimated for the included taxa and are provided as Excel-file. Groups were defined as follow: (i) subgenus <i>Lithobius</i>, (ii) subgenus <i>Monotarsobius</i>, (iii) subgenus <i>Sigibius</i>, (iv) Lithobiidae, non-<i>Lithobius</i>, and (v) Henicopidae.<br>
DOI: 10.6084/m9.figshare.12520040
title: Supplementary material 7: parsimony trees and in-/output file (morphological data)

description: Resulting parsimony trees as TREE-files together with the corresponding input-/output-files from the analyses using morphological data.
DOI: 10.6084/m9.figshare.12520040.v1
title: Supplementary material 7: parsimony trees and in-/output file (morphological data)

description: Resulting parsimony trees as TREE-files together with the corresponding input-/output-files from the analyses using morphological data.
DOI: 10.21227/2e45-zs93
title: Independent tasks

description: submitted independent tasks in the cloud computing
DOI: 10.21227/40fq-rv59
title: Independent tasks

description: submitted independent tasks in the cloud computing
DOI: 10.6084/m9.figshare.12520043
title: Supplementary material 8: parsimony trees and in-/output files (morphological and molecular data)

description: Resulting parsimony trees as TREE-files together with the corresponding input-/output-files from the analyses using morphological and molecular data.<br>
DOI: 10.6084/m9.figshare.12520043.v1
title: Supplementary material 8: parsimony trees and in-/output files (morphological and molecular data)

description: Resulting parsimony trees as TREE-files together with the corresponding input-/output-files from the analyses using morphological and molecular data.<br>
DOI: 10.6084/m9.figshare.12520055
title: Supplementary material 11: Lithobius crassipes from Hungary - leg spinulation, body length, coxal pore number

description: Morphological observations (leg spinulation, body length, coxal pore number) and comparison of specimens from Lithobius crassipes from Hungary and a specimen of Lithobius crassipes from the Natural History Museum Vienna as Excel-file.<br>
DOI: 10.6084/m9.figshare.12520055.v1
title: Supplementary material 11: Lithobius crassipes from Hungary - leg spinulation, body length, coxal pore number

description: Morphological observations (leg spinulation, body length, coxal pore number) and comparison of specimens from Lithobius crassipes from Hungary and a specimen of Lithobius crassipes from the Natural History Museum Vienna as Excel-file.<br>
DOI: 10.6084/m9.figshare.12520058
title: Supplementary material 13: Bayesian trees and in-/output files (morphological and molecular data)

description: Resulting Bayesian trees as TREE-files together with the corresponding input-/output-files from the analysis.
DOI: 10.6084/m9.figshare.12520046
title: Supplementary material 10: Lithobius crassipes and Lithobius crassipesoides - pairwise distances

description: Pairwise distances calculated for <i>Lithobius crassipes</i> and <i>Lithobius crassipesoides</i> provided as Excel-file.<br>
DOI: 10.6084/m9.figshare.12520046.v1
title: Supplementary material 10: Lithobius crassipes and Lithobius crassipesoides - pairwise distances

description: Pairwise distances calculated for <i>Lithobius crassipes</i> and <i>Lithobius crassipesoides</i> provided as Excel-file.<br>
DOI: 10.6084/m9.figshare.12520049.v1
title: Supplementary material 12: Lithobius crassipes and Lithobius crassipesoides - maximum likelihood tree

description: Maximum likelihood tree using the specimen of <i>Lithobius crassipes</i> included in our molecular analyses and other sequenced <i>Lithobius crassipes</i> (obtained from Genbank).<br>
DOI: 10.6084/m9.figshare.12520049
title: Supplementary material 12: Lithobius crassipes and Lithobius crassipesoides - maximum likelihood tree

description: Maximum likelihood tree using the specimen of <i>Lithobius crassipes</i> included in our molecular analyses and other sequenced <i>Lithobius crassipes</i> (obtained from Genbank).<br>
DOI: 10.6084/m9.figshare.12520052
title: Supplementary material 9: character tracing total evidence tree

description: P { margin-bottom: 0.21cm } Results of the tracing of all morphological characters on the total evidence tree, where every character can be visualized separately.
DOI: 10.6084/m9.figshare.12520052.v1
title: Supplementary material 9: character tracing total evidence tree

description: P { margin-bottom: 0.21cm } Results of the tracing of all morphological characters on the total evidence tree, where every character can be visualized separately.
DOI: 10.5281/zenodo.4230596
title: Data Set Sentimen Twit Terhadap Insiden Kebocoran Data Tokopedia

description: Preprocessed Indonesian Tweets Dataset about Tokopedia Databreach Incident for Sentiment Analysis Research
DOI: 10.21227/v5t8-em38
title: Model13

description: Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in consumption and temperature metadata, which we imputed using daily level linear interpolation.Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data, Three lag variable for average weather data (day1, day2, day3 lag)b) Time-Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the 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 a monthly level to get our final submission.Update- we identified those customers where our model was not performing well and used the Moving average to forecast for these customers.
DOI: 10.5281/zenodo.4245743
title: 4-Data-nop tchi

description: Data set of IJMS
DOI: 10.5281/zenodo.4245742
title: 4-Data-nop tchi

description: Data set of IJMS
DOI: 10.5281/zenodo.4246437
title: A novel clinical model for predicting malignancy of solitary pulmonary nodules: A multicenter study in Chinese population

description: <strong>Supplementary Data</strong> Supplement Figure 1: The calibration curves for the novel model in training cohort (A), internal validation cohort (B) and external validation cohort (C), respectively. Supplement Table 1. Demographics and clinical characteristics of patients from Sun Yat-sen University Cancer Center. Supplement Table 2. Demographics and clinical characteristics of patients from Henan Tumor Hospital. Supplement Table 3. Comparison of the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio of the three models analyzed in this study Supplement Table 4. The correlation between our model, PKUPH model and Mayo model