cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/rzay-dm70
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's data sharing 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 with an exact location that contained any of the previously mentioned keywords.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.If you're looking for Corona Virus (COVID-19) Tweets Dataset: http://dx.doi.org/10.21227/781w-ef42If you need the geolocation-based data starting March 20, 2020, then use the COVID-19 Tweets dataset and hydrate the IDs while adding the following condition:if data["coordinates"] is true; then use longitude, latitude = data["coordinates"]["coordinates"]
DOI: 10.6073/pasta/45211967c01dafa8a2129ae44b61c9b7
title: Jornada Basin LTER Cross-scale Interactions Study (CSIS) Block 9 meteorological station: 5-minute summary wind and air temperature data: 2013 - ongoing

description: This data package contains 5-minute air temperature and wind summary data from Jornada Basin LTER Cross Scale Interactions Study (CSIS) Block-9 met station. Average air temperature, wind speed, and wind direction are measured and calculated at an automated meteorological station connected to a 900 MHz spread spectrum wireless network. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximate 2.5m. This is a subset of baseline weather data collected at the 15 Jornada Basin LTER CSIS study blocks, New Mexico. Measurements are aggregated at intervals of 5, 30, 60 minutes and daily, with precipitation additionally provided with 1 second aggregation during precipitation events. The met station variables measured include air temperature, precipitation, relative humidity, wind direction, wind speed. Precipitation is not measured at Block 1 and Block 12 because of proximity to Block 2 and Block 13, respectively . This is an ONGOING dataset.
DOI: 10.15161/oar.it/23627
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.25345/c55x24
title: MassIVE MSV000085360 - 15_fungal isolates_bioconversion_EGCG
title: Dark teas are prepared by a microbial fermentation process. Flavan-3-ol B-ring fission analogues (FBRFAs) are some of the key bioactive constituents that characterise dark teas. The microbes and biosynthetic precursors involved in the formation of FBRFAs are not known. Using a unique solid-state fermentation system with beta-cyclodextrin inclusion complexation, as well as targeted chromatographic isolation, non-targeted spectroscopic identification, and Feature-based Molecular Networking (FBMN) on the Global Natural Products Social Molecular Networking (GNPS) web-platform, we reveal that dihydromyricetin and the FBRFAs, including teadenol A and fuzhuanin A, are derived from epigallocatechin gallate upon exposure to fungal strains isolated from Fuzhuan brick tea. In particular the strains from subphylum Pezizomycotina were key drivers for these B-/C-ring oxidation transformations. These are the same transformations seen during the fermentation process of dark teas. These discoveries set the stage to enrich dark teas and other food products for these health promoting constituents.
DOI: 10.6084/m9.figshare.12234428.v1
title: A new environment-aware scheduling method for remanufacturing system with non-dedicated reprocessing lines using improved flower pollination algorithm

description: This is the experimental data of the manuscript entitled “A new environment-aware scheduling method for remanufacturing system with non-dedicated reprocessing lines using improved flower pollination algorithm”. All data are generated through computer simulation and saved in the file with csv format. There are two datasets for these experiments, which include 17 instances in total. The datasets show the carbon emission signature, the weights for two objectives, the time to complete the corresponding operations in the corresponding workstations or reprocessing cells. The number of disassembly workstations, reassembly workstations, and reprocessing cells in each reprocessing line, the power consumptions of reassembly workstations and reprocessing cells in idle status and start-stop status, and the start-up time of reassembly workstations and reprocessing cells in each reprocessing line also shows in the datasets.
DOI: 10.21227/n57s-d646
title: Non-Coherent Data-Aided Iterative Reference Enhancement Technique Excluding the Symbol Under Demodulation

description: Including the symbol under demodulation in data-aided reference/pilot recovery process, dominates the contribution of all the received sybmols. Therefore, excluding the contribution of the symbol under detection allows other symbols to equally contributeto the reference estimation.
DOI: 10.21227/wmsd-xm89
title: Non-Coherent Data-Aided Iterative Reference Enhancement Technique Excluding the Symbol Under Demodulation

description: Including the symbol under demodulation in data-aided reference/pilot recovery process, dominates the contribution of all the received sybmols. Therefore, excluding the contribution of the symbol under detection allows other symbols to equally contributeto the reference estimation.
DOI: 10.5281/zenodo.3781395
title: 3D MTHFR models in different eukaryotic species

description: 3D models for <em>Mus musculus, Gallus gallus, Danio rerio, Acanthaster planci, Arabidopsis thaliana, C.elegans MTHFR </em> protein
DOI: 10.5281/zenodo.3781396
title: 3D MTHFR models in different eukaryotic species

description: 3D models for <em>Mus musculus, Gallus gallus, Danio rerio, Acanthaster planci, Arabidopsis thaliana, C.elegans MTHFR </em> protein
DOI: 10.5281/zenodo.3782389
title: Linked COVID-19 Data: Johns Hopkins University (JHU) and European Centre for Disease Prevention and Control (ECDC)

description: Linked COVID-19 Data derived from <strong>Johns Hopkins University</strong> and <strong>European Centre for Disease Prevention and Control</strong> using the COVID-19 Ontology 10.5281/zenodo.3757828 developed for the Linked COVID-19 Data Dashboard: http://covid19data.link This files include data for covid19_jhu.ttl - COVID-19 data collected by the JHU covid19_ecdc.ttl - COVID-19 data collected by the ECDC This RDF files are based on https://pomber.github.io/covid19/timeseries.json https://opendata.ecdc.europa.eu/covid19/casedistribution/json/
DOI: 10.5281/zenodo.3782390
title: Linked COVID-19 Data: Robert Koch-Institut (RKI)

description: Linked COVID-19 Data derived from <strong>Robert Koch Institute</strong> using the COVID-19 Ontology 10.5281/zenodo.3757828 developed for the Linked COVID-19 Data Dashboard: http://covid19data.link This files include data for covid19_rki*.ttl - single COVID-19 cases per day, collected by the RKI https://www.arcgis.com/home/item.html?id=dd4580c810204019a7b8eb3e0b329dd6 "confirmed" is calcaulated by the column "Refdatum" covid19_rki_ger.ttl - COVID-19 cases per day for Germany, collected by the RKI covid19_rki_ger_cum.ttl - COVID-19 cases accumulated per day for Germany, collected by the RKI covid19_rki_fs.ttl - COVID-19 cases per day for the federal states in Germany, collected by the RKI covid19_rki_fs_cum.ttl - COVID-19 cases accumulated per day for the federal states in Germany, collected by the RKI This RDF files are based on the NPGEO Corona Hub 2020 https://opendata.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0.geojson https://npgeo-corona-npgeo-de.hub.arcgis.com/datasets/dd4580c810204019a7b8eb3e0b329dd6_0/data
DOI: 10.21227/frnn-t327
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: If you need the geolocation-based data starting March 20, 2020, then use the Corona Virus (COVID-19) Tweets dataset and hydrate the IDs while adding the following condition:if data["coordinates"] is true; use longitude, latitude = data["coordinates"]["coordinates"]This dataset contains the geolocation-based sentiment analysis data. The data is generated by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's data sharing 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 with an exact location that contained any of the previously mentioned keywords.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.If you're looking for Corona Virus (COVID-19) Tweets Dataset: http://dx.doi.org/10.21227/781w-ef42
DOI: 10.21227/k3ya-vk31
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: If you need the geolocation-based data starting March 20, 2020, then use the Corona Virus (COVID-19) Tweets dataset and hydrate the IDs while adding the following condition:if data["coordinates"] is true; use longitude, latitude = data["coordinates"]["coordinates"]This dataset contains the geolocation-based sentiment analysis data. The data is generated by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's data sharing 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 with an exact location that contained any of the previously mentioned keywords.The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.If you're looking for Corona Virus (COVID-19) Tweets Dataset: http://dx.doi.org/10.21227/781w-ef42
DOI: 10.5281/zenodo.3782401
title: Data set | Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico (v1.1)

description: This repository contains raw and processed data along with the Matlab scripts used to prepare the visualizations presented in the manuscript Bücker, M., Flores Orozco, A., Gallistl, J., Steiner, M., Aigner, L., Hoppenbrock, J., Glebe, R., Morales Barrera, W., Pita de la Paz, C., García García, E., Razo Pérez, J.A., Buckel, J., Hördt, A., Schwalb, A., and Perez, L. (2020). <strong><em>Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico</em></strong>. Submitted to Solid Earth. If you find this data useful in your own research, please mention this data set and/or the manuscript.
DOI: 10.5281/zenodo.3782402
title: Data set | Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico (v1.1)

description: This repository contains raw and processed data along with the Matlab scripts used to prepare the visualizations presented in the manuscript Bücker, M., Flores Orozco, A., Gallistl, J., Steiner, M., Aigner, L., Hoppenbrock, J., Glebe, R., Morales Barrera, W., Pita de la Paz, C., García García, E., Razo Pérez, J.A., Buckel, J., Hördt, A., Schwalb, A., and Perez, L. (2020). <strong><em>Water- and land-borne geophysical surveys before and after the sudden water-level decrease of two large karst lakes in southern Mexico</em></strong>. Submitted to Solid Earth. If you find this data useful in your own research, please mention this data set and/or the manuscript.
DOI: 10.5281/zenodo.3969539
title: Trade and Domestic Production Networks

description: This package contains the code and public access data used to produce the figures and tables in Dhyne, Kikkawa, Mogstad, and Tintelnot (forthcoming), Trade and Domestic Production Networks, Review of Economic Studies.
DOI: 10.5281/zenodo.3596464
title: Data from: Heterosis of leaf and rhizosphere microbiomes in field-grown maize

description: Data and code associated with the submitted manuscript "<strong>Heterosis of leaf and rhizosphere microbiomes in field-grown maize</strong>". Detailed descriptions of each file can be found in the README.txt . The raw sequence data associated with this work can be downloaded from the NCBI Sequence Read Archive, listed under BioProject #PRJNA597058.
description: M.R.W. was supported by a NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology (IOS-1612951). This work was also funded by a NCSU Plant Soil Microbial Community Consortium grant to J.B.H. and M.R.W.
DOI: 10.5281/zenodo.3596465
title: Data from: Heterosis of leaf and rhizosphere microbiomes in field-grown maize

description: Data and code associated with the submitted manuscript "<strong>Heterosis of leaf and rhizosphere microbiomes in field-grown maize</strong>". Detailed descriptions of each file can be found in the README.txt . The raw sequence data associated with this work can be downloaded from the NCBI Sequence Read Archive, listed under BioProject #PRJNA597058.
description: M.R.W. was supported by a NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology (IOS-1612951). This work was also funded by a NCSU Plant Soil Microbial Community Consortium grant to J.B.H. and M.R.W.
DOI: 10.5281/zenodo.3782732
title: sphinx_015.tar.gz

description: 15/33 Captcha data.
DOI: 10.5281/zenodo.3782733
title: sphinx_015.tar.gz

description: 15/33 Captcha data.
DOI: 10.21227/w9y5-8k89
title: ICRI Envirosensor Data

description: The Here East Digital Twin was a six month trial of a real-time 3D data visualisation platform, designed for the purpose of supporting operational management in the built environment. The trial used eighteen custom sensing devices created by the Intel Collaborative Research Institute (ICRI) for Urban IoT. Data from the sensors were aggregated in a cloud-based platform which then transmitted the data to specially designed client applications which visualised the data in the context of a real-time 3D model or 'Digital Twin'. The Digital Twin was accessible to site occupants and visitors in situ using WebGL. In this way a shared view of site operation was made available to building managers, occupants and visitors.This data collection provides 22 weeks of raw sensor readings captured at a frequency of one reading (per sensor) every sixty seconds from Ti CC2650STK SensorTags. Readings include:Ambient Light (OPT / OPT3001)Ambient Temperature (TMP / TMP007)Temperature (BAT / BMP280)Ambient Temperature (HDT / HDC1000)Barometric Pressure (BAR / BMP280)Humidity (HDH / HDC1000)The data are made available for experimentation in data visualisation, data modelling, simulation, IoT data pipeline development or machine learning.NOTE: Data were captured under operational site conditions as part of an 'in the wild' deployment. Data are provided as read from the sensor. Sensors may have been moved or tampered with while onsite. They may also have suffered faults or experienced downtime.Thank you to the Intel Collaborative Research Institute (ICRI) for Urban IoT for providing the Envirosensor platform. This project is linked to research at CASA funded by the Engineering and Physical Science Research Council (EPSRC) and Ordnance Survey (OS), the national mapping agency for Great Britain.
DOI: 10.5281/zenodo.3780386
title: Molecular dynamics simulation data of regulatory ACT domain dimer mutation (E76G) of human phenylalanine hydroxylase (PAH)

description: Raw data of molecular dynamics simulations of regulatory ACT domain dimer mutation (E76G). <strong>binding.zip</strong>: simulation of dimer with 19 Phe ligand <strong>bound.zip</strong>: simulation of dimer with bound Phe ligand <strong>dimer.zip</strong>: simulation of dimer only Simulation setup files are also included in each folder.
DOI: 10.5281/zenodo.3780387
title: Molecular dynamics simulation data of regulatory ACT domain dimer mutation (E76G) of human phenylalanine hydroxylase (PAH)

description: Raw data of molecular dynamics simulations of regulatory ACT domain dimer mutation (E76G). <strong>binding.zip</strong>: simulation of dimer with 19 Phe ligand <strong>bound.zip</strong>: simulation of dimer with bound Phe ligand <strong>dimer.zip</strong>: simulation of dimer only Simulation setup files are also included in each folder.
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m.2
title: C39H32AuClNP
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: This dataset contains experimental data from a sample (InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1). The datasets from the following experiments are available: 13C nuclear magnetic resonance spectroscopy (13C NMR) 1H nuclear magnetic resonance spectroscopy (1H NMR) 31P nuclear magnetic resonance spectroscopy (31P NMR) infrared absorption spectroscopy (IR) electrospray ionisation quadrupole detection (ESI Q Trap)
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m/chmo0000593.1
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, CDCl3, ppm) δ = 8.74–8.73 (m, 1H, HAr), 8.16–8.03 (m, 2H, HAr), 7.89–7.75 (m, 2H, HAr), 7.65–7.56 (m, 5H, HAr), 7.56–7.51 (m, 3H, HAr), 7.51–7.46 (m, 4H, HAr), 7.41–7.36 (m, 2H, HAr), 7.29–7.25 (m, 1H, HAr), 6.77 (d, J = 2.0 Hz, 1H, HAr), 6.71 (d, J = 7.7 Hz, 1H, HAr), 6.34 (d, J = 7.9 Hz, 1H, HAr), 6.00 (dd, J = 14.7, 1.7 Hz, 1H, HAr), 3.91 – 3.75 (m, 2H, HPc), 3.33 (ddd, J = 13.8, 10.2, 5.0 Hz, 1H, HPc), 3.08 (ddd, J = 12.4, 11.0, 2.3 Hz, 1H, HPc), 2.97 (dddd, J = 17.3, 13.9, 10.4, 3.4 Hz, 2H, HPc), 2.77 (ddd, J = 13.6, 10.1, 3.6 Hz, 1H, HPc), 2.55 (ddd, J = 13.5, 10.3, 5.0 Hz, 1H, HPc). Impurities: The spectrum contains 5% ethyl acetate.
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m/chmo0000739
title: 31P nuclear magnetic resonance spectroscopy (31P NMR) ()
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: dataset for 31P nuclear magnetic resonance spectroscopy (31P NMR) 31P NMR (202 MHz, CDCl3, ppm) δ = 29.77 (d, J = 14.0 Hz).
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m/chmo0002337
title: electrospray ionisation quadrupole detection (ESI Q Trap) ()
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: dataset for electrospray ionisation quadrupole detection (ESI Q Trap) HRMS (ESI+, [M]+, C39H33AuClNP) Calcd 778.1699; Found: 778.1690
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (126 MHz, CDCl3, ppm) δ = 157.1 (Cq, CAr), 149.7 (+, CH, CAr), 144.4 (d, J = 10.7 Hz, Cq, CAr), 141.8 (Cq, CAr), 141.7 (Cq, CAr), 141.0 (d, J = 10.4 Hz, Cq, CAr), 139.6 (Cq, CAr), 138.0 (Cq, CAr), 137.2 (Cq, CAr), 137.0 (+, CH, CAr), 136.6 (+, d, J = 7.6 Hz, CH, CAr), 135.6 (+, d, J = 10.0 Hz, CH, CAr), 135.3 (+, d, J = 14.1 Hz, 2C, CH, CAr), 135.0 (+, CH, CAr), 134.2 (+, d, J = 13.8 Hz, 2C, CH, CAr), 133.8 (+, d, J = 3.1 Hz, CH, CAr), 132.2 (+, CH, CAr), 132.2 (+, CH, CAr), 132.0 (+, d, J = 2.7 Hz, CH, CAr), 131.4 (+, CH, CAr), 130.5 (d, J = 64.4 Hz, Cq, CAr), 130.2 (+, 2C, CH, CAr), 129.4 (+, d, J = 11.9 Hz, 2C, CH, CAr), 129.1 (+, d, J = 12.1 Hz, 2C, CH, CAr), 129.0 (d, J = 63.5 Hz, Cq, CAr), 127.4 (+, 2C, CH, CAr), 126.5 (d, J = 63.1 Hz, Cq, CAr), 122.4 (+, CH, CAr), 120.8 (+, CH, CAr), 34.9 (–, d, J = 4.6 Hz, CH2), 34.7 (–, d, J = 8.6 Hz, CH2), 33.1 (–, CH2), 30.6 (–, CH2). Impurities: The spectrum contains 5% ethyl acetate according to 1H NMR.
DOI: 10.14272/lrplbhmnpskbiq-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C39H32NP.Au.ClH/c1-3-9-35(10-4-1)41(36-11-5-2-6-12-36)39-28-30-15-19-31-18-14-29(16-20-34(39)21-17-30)27-37(31)32-22-24-33(25-23-32)38-13-7-8-26-40-38;;/h1-14,17-18,21-28H,15-16,19-20H2;;1H/q;+1;/p-1

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, cm-1) ṽ = 3050 (vw), 3003 (vw), 2921 (w), 2851 (vw), 1732 (w), 1608 (vw), 1584 (m), 1572 (w), 1463 (m), 1434 (s), 1412 (w), 1391 (w), 1309 (w), 1296 (w), 1238 (w), 1183 (w), 1153 (w), 1098 (m), 1011 (w), 993 (w), 911 (w), 849 (m), 782 (s), 742 (vs), 710 (m), 691 (vs), 652 (m), 616 (w), 571 (vw), 548 (m), 523 (m), 510 (vs), 490 (vs), 443 (w), 426 (w), 401 (w).