cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/0d2t-7j78
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 256,624 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.5281/zenodo.4090153
title: Lake ice thickness dataset

description: This dataset consist of three products including: (1) Lake ice thickness of 11 large lakes measured by satellite altimeters for 2002-2019; (2) Lake ice thickness and lake surface snow depth of 1260 lakes with an area &gt; 50 km<sup>2</sup> in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018; (3) Future lake ice thickness and surface snow depth for 2091-2099 modeled by the lake ice model with a modified ice growth module. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of fresh water ice as well as its response to climate change.
DOI: 10.6084/m9.figshare.13199777
title: AGU_SI_Cov19-GRL.zip

description: This file contains the Appendix A and calculated aircraft emissions
DOI: 10.6084/m9.figshare.13199777.v1
title: AGU_SI_Cov19-GRL.zip

description: This file contains the Appendix A and calculated aircraft emissions
DOI: 10.21227/mqm2-w782
title: Output Feedback Controller Design for Discrete LTI Systems with Polytopic Uncertainty via Direct Searching of the Design Space

description: This paper concerns static output feedback stabilization of polytopic discrete LTI systems. The previous related studies were mainly based on LMI approaches which are naturally conservative. In this paper, a novel design algorithm is presented that iteratively partitions a primary design space to subspaces. Then, by assessing stabilizability status of each generated subspace, the algorithm determines the total stabilizable parts and removes the undesired parts of the design space. Mathematical theories are developed to predict the total De-stabilizability or stabilizability of a given subspace. These subspaces’ properties are detected through checking the existence of critical polynomials (which have roots on the unit circle of the complex plane) on their exposed edges. By omitting the undesired parts of the design space, the algorithm just searches the desired parts which are far smaller than the primary design space. This strategy improves the feasibility performance of the algorithm. Some illustrating examples are provided to show the steps and iterations of the design algorithm. Furthermore, one hundred random models are generated to evaluate the feasibility performance of the proposed algorithm as compared to some existing methods. The results reveal the superiority of the proposed algorithm.
DOI: 10.21227/7tck-na46
title: Output Feedback Controller Design for Discrete LTI Systems with Polytopic Uncertainty

description: This paper concerns static output feedback stabilization of polytopic discrete LTI systems. The previous related studies were mainly based on LMI approaches which are naturally conservative. In this paper, a novel design algorithm is presented that iteratively partitions a primary design space to subspaces. Then, by assessing stabilizability status of each generated subspace, the algorithm determines the total stabilizable parts and removes the undesired parts of the design space. Mathematical theories are developed to predict the total De-stabilizability or stabilizability of a given subspace. These subspaces’ properties are detected through checking the existence of critical polynomials (which have roots on the unit circle of the complex plane) on their exposed edges. By omitting the undesired parts of the design space, the algorithm just searches the desired parts which are far smaller than the primary design space. This strategy improves the feasibility performance of the algorithm. Some illustrating examples are provided to show the steps and iterations of the design algorithm. Furthermore, one hundred random models are generated to evaluate the feasibility performance of the proposed algorithm as compared to some existing methods. The results reveal the superiority of the proposed algorithm.
DOI: 10.6084/m9.figshare.13199822.v1
title: ADS-B_Appendix A_Aircraft emission

description: This file contains the Appendix A and calculated aircraft emissions.
DOI: 10.6084/m9.figshare.13199822
title: ADS-B_Appendix A_Aircraft emission

description: This file contains the Appendix A and calculated aircraft emissions.
DOI: 10.5281/zenodo.4245727
title: The dataset of the research about DO

description: This dataset includes in-situ observations obtained by Chinese underwater glider, sea surface temperature data, wind vector data, HYCOM modeling data, sea level anomaly data and typhoon best track data.
DOI: 10.5281/zenodo.4245726
title: The dataset of the research about DO

description: This dataset includes in-situ observations obtained by Chinese underwater glider, sea surface temperature data, wind vector data, HYCOM modeling data, sea level anomaly data and typhoon best track data.
DOI: 10.25959/5fa4ef3dcd222
title: Australian waste management changes
DOI: 10.21227/3f96-c342
title: Model15

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: 10.26182/rkcy-qa46
title: Amborella pangenome bam files
DOI: 10.34740/kaggle/dsv/1623205
title: COVID-19 cases by Continent
DOI: 10.18154/rwth-2020-10467
title: Online monitoring applying the anaerobic respiratory monitoring system reveals iron(II) limitation in YTF medium for Clostridium ljungdahlii

description: This data set represents raw data recorded for the journal article entitled "Online monitoring applying the anaerobic respiratory monitoring system reveals iron(II) limitation in YTF medium for Clostridium ljungdahlii", published in engineering in life sciences. The raw data of the carbon dioxide tranfer rate (CTR) as well as offline measured data is available. A detailed data describtion and analysis is available in the original manuscript. All detailes on data generation are available in the manuscript aswell.
DOI: 10.21227/6r8a-mg38
title: Model16

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: 10.23729/81984418-4b8d-4697-8828-69116906d387
title: Biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon

description: This dataset contains biomechanical, biochemical, and near infrared spectral data of bovine knee ligaments and patellar tendon. The data was obtained via tensile testing, colorimetric biochemical methods and near infrared spectroscopy measurements of sample pieces taken from the mid-substance of these tissues.
DOI: 10.15468/p5d734
title: Identification guide to larvae of Caucasian Epeorus (Caucasiron) (Ephemeroptera, Heptageniidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hrivniak, Ľubos, Sroka, Pavel, Bojkova, Jindriska, Godunko, Roman J. (2020): Identification guide to larvae of Caucasian Epeorus (Caucasiron) (Ephemeroptera, Heptageniidae). ZooKeys 986: 1-53, DOI: http://dx.doi.org/10.3897/zookeys.986.56276, URL: http://dx.doi.org/10.3897/zookeys.986.56276
DOI: 10.15468/z43d9c
title: Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea

description: This dataset contains the digitized treatments in Plazi based on the original journal article Fatihah-Syafiq, Muhamad, Badli-Sham, Baizul Hafsyam, Fahmi-Ahmad, Muhammad, Aqmal-Naser, Mohamad, Rizal, Syed Ahmad, Azmi, Mohd Shahrizan Azrul, Grismer, Larry L., Ahmad, Amirrudin B. (2020): Checklist of herpetofauna in the severely degraded ecosystem of Bidong Island, Peninsular Malaysia, South China Sea. ZooKeys 985: 143-162, DOI: http://dx.doi.org/10.3897/zookeys.985.54737, URL: http://dx.doi.org/10.3897/zookeys.985.54737
DOI: 10.15468/9vafe9
title: Revision of the South American genus Gaujonia Dognin (Noctuidae, Pantheinae) with descriptions of five new genera and twenty-one new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Martinez, Jose I. (2020): Revision of the South American genus Gaujonia Dognin (Noctuidae, Pantheinae) with descriptions of five new genera and twenty-one new species. ZooKeys 985: 71-126, DOI: http://dx.doi.org/10.3897/zookeys.985.51622, URL: http://dx.doi.org/10.3897/zookeys.985.51622
DOI: 10.15468/t7tf46
title: New records of Celaenorrhinus pyrrha de Niceville, 1889 and C. munda (Moore, 1884) from China (Lepidoptera, Hesperiidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Xue, Gou-Xi, Inayoshi, Yutaka, Li, Meng, Zhang, Fu-Ming, Lai, Da-Kun, Tian, Hai-Ying (2020): New records of Celaenorrhinus pyrrha de Niceville, 1889 and C. munda (Moore, 1884) from China (Lepidoptera, Hesperiidae). ZooKeys 985: 61-70, DOI: http://dx.doi.org/10.3897/zookeys.985.46835, URL: http://dx.doi.org/10.3897/zookeys.985.46835
DOI: 10.15468/unefc6
title: Description of the new species Coptera tonic (Hymenoptera, Diapriidae), a pupal parasitoid of Rhagoletis juniperina Marcovitch (Diptera, Tephritidae), and revised partial keys to Nearctic Coptera Say

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ericson, Hannah C., Forbes, Andrew A. (2020): Description of the new species Coptera tonic (Hymenoptera, Diapriidae), a pupal parasitoid of Rhagoletis juniperina Marcovitch (Diptera, Tephritidae), and revised partial keys to Nearctic Coptera Say. ZooKeys 985: 49-60, DOI: http://dx.doi.org/10.3897/zookeys.985.56974, URL: http://dx.doi.org/10.3897/zookeys.985.56974
DOI: 10.15468/68vymb
title: A reassessment of the genus Oligoneuriopsis Crass, 1947 (Ephemeroptera, Oligoneuriidae, Oligoneuriellini)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Barber-James, Helen M., Zrelli, Sonia, Yanai, Zohar, Sartori, Michel (2020): A reassessment of the genus Oligoneuriopsis Crass, 1947 (Ephemeroptera, Oligoneuriidae, Oligoneuriellini). ZooKeys 985: 15-47, DOI: http://dx.doi.org/10.3897/zookeys.985.56649, URL: http://dx.doi.org/10.3897/zookeys.985.56649
DOI: 10.15468/usfd2f
title: Four new species of the genus Diduga Moore, [1887] (Lepidoptera, Erebidae, Arctiinae) from China and Malaysia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhao, Ting Ting, Han, Hui Lin (2020): Four new species of the genus Diduga Moore, [1887] (Lepidoptera, Erebidae, Arctiinae) from China and Malaysia. ZooKeys 985: 127-141, DOI: http://dx.doi.org/10.3897/zookeys.985.54047, URL: http://dx.doi.org/10.3897/zookeys.985.54047
DOI: 10.21227/qbns-xe19
title: Model17

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: 10.6084/m9.figshare.1141971.v7
title: GHE

description: contains R-code used for the figures and analysis presented in 'The greenhouse effect seen in old physics and new data'
DOI: 10.6084/m9.figshare.13199954.v1
title: Dry.xlsx

description: Dry electrodes measured over a three minute period.
DOI: 10.6084/m9.figshare.13199954
title: Dry.xlsx

description: Dry electrodes measured over a three minute period.
DOI: 10.5281/zenodo.4247969
title: Soil Moisture Active/Passive (SMAP) Level 4 Carbon (L4C) Nature Run version 7.2

description: The Soil Moisture Active/ Passive (SMAP) Level 4 Carbon (L4C) product is a daily, global, terrestrial carbon budget driven, in part, by soil moisture estimates from the Level 4 Soil Moisture (L4SM) product and, in turn, on brightness temperature observations from the SMAP satellite [1,2]. The SMAP L4C operational product's record begins on March 31, 2015, shortly after the launch of SMAP, and continues to the present, with an average latency of 9 days [3]. SMAP L4C data are posted to a global, 9-km equal-area EASE-Grid 2.0 [4]. In order to improve the longitudinal coverage of the SMAP L4C record, a model-only "Nature Run" was devised, with daily carbon budget estimates beginning January 1, 2000. The Nature Run differs from the SMAP L4C operational product in the following ways: - The SMAP L4C Nature Run uses the MERRA-2 re-analysis dataset for meteorological driver data, instead of the GEOS-5 FP driver data used in the operational product.<br> - The SMAP L4C Nature Run uses soil moisture and soil temperature estimates from the L4SM Nature Run, which is a model-only version of the operational L4SM product that does not assimilate SMAP brightness temperature data. This repository contains the full README for the data. The data can be downloaded from: http://files.ntsg.umt.edu/data/SMAP_L4C_NatureRun/NRv7.2/
description: {"references": ["Jones, L. A., J. S. Kimball, R. H. Reichle, N. Madani, J. Glassy, J. V Ardizzone, A. Colliander, J. Cleverly, A. R. Desai, D. Eamus, E. S. Euskirchen, L. Hutley, C. Macfarlane, and R. L. Scott. 2017. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange. IEEE Transactions on Geoscience and Remote Sensing 55 (11):6517\u20136532."]}
DOI: 10.5281/zenodo.4247968
title: Soil Moisture Active/Passive (SMAP) Level 4 Carbon (L4C) Nature Run version 7.2

description: The Soil Moisture Active/ Passive (SMAP) Level 4 Carbon (L4C) product is a daily, global, terrestrial carbon budget driven, in part, by soil moisture estimates from the Level 4 Soil Moisture (L4SM) product and, in turn, on brightness temperature observations from the SMAP satellite [1,2]. The SMAP L4C operational product's record begins on March 31, 2015, shortly after the launch of SMAP, and continues to the present, with an average latency of 9 days [3]. SMAP L4C data are posted to a global, 9-km equal-area EASE-Grid 2.0 [4]. In order to improve the longitudinal coverage of the SMAP L4C record, a model-only "Nature Run" was devised, with daily carbon budget estimates beginning January 1, 2000. The Nature Run differs from the SMAP L4C operational product in the following ways: - The SMAP L4C Nature Run uses the MERRA-2 re-analysis dataset for meteorological driver data, instead of the GEOS-5 FP driver data used in the operational product.<br> - The SMAP L4C Nature Run uses soil moisture and soil temperature estimates from the L4SM Nature Run, which is a model-only version of the operational L4SM product that does not assimilate SMAP brightness temperature data. This repository contains the full README for the data. The data can be downloaded from: http://files.ntsg.umt.edu/data/SMAP_L4C_NatureRun/NRv7.2/
description: {"references": ["Jones, L. A., J. S. Kimball, R. H. Reichle, N. Madani, J. Glassy, J. V Ardizzone, A. Colliander, J. Cleverly, A. R. Desai, D. Eamus, E. S. Euskirchen, L. Hutley, C. Macfarlane, and R. L. Scott. 2017. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange. IEEE Transactions on Geoscience and Remote Sensing 55 (11):6517\u20136532."]}
DOI: 10.21227/6wvb-6767
title: Model18

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: 10.6084/m9.figshare.13199966
title: Supplementary Material for: New Homozygous Missense <b><i>MSMO1</i></b> Mutation in Two Siblings with SC4MOL Deficiency Presenting with Psoriasiform Dermatitis

description: Sterol-C4-methyl oxidase (SC4MOL) deficiency was recently described as an autosomal recessive cholesterol biosynthesis disorder caused by mutations in the <i>MSMO1</i> (sometimes also referred to as <i>SC4MOL</i>) gene. To date, 5 patients from 4 unrelated families with SC4MOL deficiency have been reported. Diagnosis can be challenging as the biochemical accumulation of methylsterols can affect global development and cause skin and ocular pathology. Herein, we describe 2 siblings from a consanguineous Turkish family with SC4MOL deficiency presenting with psoriasiform dermatitis, ocular abnormalities (nystagmus, optic hypoplasia, myopia, and strabismus), severe intellectual disability, and growth and motor delay. We undertook whole-exome sequencing and identified a new homozygous missense mutation c.81A&gt;C; p.Asn27Thr in <i>MSMO1</i>. Segregation analysis in all available family members confirmed recessive inheritance of the mutation. The siblings were treated with a combination of oral and topical statin and cholesterol which resulted in clinical improvement. This study demonstrates how genomics-based diagnosis and therapy can be helpful in clinical practice.
DOI: 10.6084/m9.figshare.13199966.v1
title: Supplementary Material for: New Homozygous Missense <b><i>MSMO1</i></b> Mutation in Two Siblings with SC4MOL Deficiency Presenting with Psoriasiform Dermatitis

description: Sterol-C4-methyl oxidase (SC4MOL) deficiency was recently described as an autosomal recessive cholesterol biosynthesis disorder caused by mutations in the <i>MSMO1</i> (sometimes also referred to as <i>SC4MOL</i>) gene. To date, 5 patients from 4 unrelated families with SC4MOL deficiency have been reported. Diagnosis can be challenging as the biochemical accumulation of methylsterols can affect global development and cause skin and ocular pathology. Herein, we describe 2 siblings from a consanguineous Turkish family with SC4MOL deficiency presenting with psoriasiform dermatitis, ocular abnormalities (nystagmus, optic hypoplasia, myopia, and strabismus), severe intellectual disability, and growth and motor delay. We undertook whole-exome sequencing and identified a new homozygous missense mutation c.81A&gt;C; p.Asn27Thr in <i>MSMO1</i>. Segregation analysis in all available family members confirmed recessive inheritance of the mutation. The siblings were treated with a combination of oral and topical statin and cholesterol which resulted in clinical improvement. This study demonstrates how genomics-based diagnosis and therapy can be helpful in clinical practice.