cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2y3bdtwdmb.1
title: Effects of Cranial Base Release and Upper Cervical Spine Mobilization in Patients with Migraine Headache: A Two group pretest posttest randomized pilot clinical trial

description: BACKGROUND: Migraine is a common chronic disorder causing recurrent attacks and thus resulting in severe complications. Patients with migraine headache suffer with reduced quality of life in turn having low productivity in workplaces, causing a burden on them. AIM: The aim of the study is to determine the effects of Cranial base release (CBR) combined with upper cervical spine mobilization and CBR only in patients with migraine. MATERIALS AND METHODS: A two group pre post pilot study was conducted. Total numbers of 12 patients suffering with migraine headache were recruited for the study. Age group of the patients is taken from 18-30 years. Patients were randomly allocated in two groups - Group A and Group B. In group A, Cranial Base Release with upper cervical spine Mobilization at (C0-C3) were given as a treatment while in group B only Cranial Base Release was as a treatment. In both the groups, treatment duration was 20 minutes for 2 weeks, 6 sessions were given for alternate days. Outcome measures used for Pre-test Post-test comparison were Pressure Algometer for measuring Pain Pressure Threshold and Headache Disability Index (HDI) for calculating Disability Index. RESULTS: To determine the normality Shapiro Wilk was used, as the sample size was less than 50. Data following normal distribution. Paired t test was used for within the group analysis and Independent t test was used for between the group analysis. Effect size was calculated form Effect Size Index. No statistically significant difference were noted in post Headache Disability Index and Algometer values (P&gt;0.05). Effect size predicts huge effect size in post HDI values in Group 1 as compared with the group 2. CONCLUSION: Both the treatments help in reducing pain and disability in patients suffering with migraine.
DOI: 10.21227/yfww-da71
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. 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 these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3706710
title: Particulate organic carbon concentration in seawater profiles collected on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).

description: <strong>Dataset abstract</strong> Seawater samples were collected from a Conductivity, Temperature, Depth (CTD) rosette, which was deployed from the R/V Akademik Tryoshnikov. The samples were collected at a number of locations in the Southern Ocean as part of the Antarctic Circumnavigation Expedition (ACE) during the expedition that took place in the Austral summer of 2016/2017. Particulate organic matter (POC) is inclusive of all living and detrital material. It is the most common form of organic carbon matter that results from photosynthesis by phytoplankton. Seawater samples were filtered during the expedition and analysed post-cruise to provide this dataset of POC concentration profiles. <strong>Dataset contents</strong> ace_ctd_particulate_organic_carbon_concentration.csv, data file, comma-separated values data_file_header.txt, metadata, text README.txt, metadata, text <strong>Dataset license</strong> This particulate organic carbon concentration in seawater dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.
description: {"references": ["https://stableisotopefacility.ucdavis.edu/13cand15n.html", "NIST Standard Reference Materials", "David W H Walton, & Jenny Thomas. (2018, November 22). Cruise Report - Antarctic Circumnavigation Expedition (ACE) 20th December 2016 - 19th March 2017 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1443511"]}
DOI: 10.5281/zenodo.3706709
title: Particulate organic carbon concentration in seawater profiles collected on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).

description: <strong>Dataset abstract</strong> Seawater samples were collected from a Conductivity, Temperature, Depth (CTD) rosette, which was deployed from the R/V Akademik Tryoshnikov. The samples were collected at a number of locations in the Southern Ocean as part of the Antarctic Circumnavigation Expedition (ACE) during the expedition that took place in the Austral summer of 2016/2017. Particulate organic matter (POC) is inclusive of all living and detrital material. It is the most common form of organic carbon matter that results from photosynthesis by phytoplankton. Seawater samples were filtered during the expedition and analysed post-cruise to provide this dataset of POC concentration profiles. <strong>Dataset contents</strong> ace_ctd_particulate_organic_carbon_concentration.csv, data file, comma-separated values data_file_header.txt, metadata, text README.txt, metadata, text <strong>Dataset license</strong> This particulate organic carbon concentration in seawater dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.
description: {"references": ["https://stableisotopefacility.ucdavis.edu/13cand15n.html", "NIST Standard Reference Materials", "David W H Walton, & Jenny Thomas. (2018, November 22). Cruise Report - Antarctic Circumnavigation Expedition (ACE) 20th December 2016 - 19th March 2017 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1443511"]}
DOI: 10.21227/q453-ba26
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. 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 these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3892637
title: 3d Virtual Patho-Histology of Lung Tissue from Covid19 Patients based on Phase Contrast X-ray Tomography

description: X-ray phase contrast tomographys datasets of lung tissue from 6 Covid-19 patients (paraffin embedded unstained tissue), and one control tissue (healthy lung, in fixative solution). The data has been recorded as part of the Covid-19 Call for Proposals at DESY April 2020 at the GINIX instrument, beamline P10 of the PETRAIII storage ring at 8keV. Three different datasets are included in a single tar archive for each sample: (i) overview scan (up to 4mm cross section ) from stitching different tomograms, (ii) 1mm cylindrical punch, and (iii) cone-beam zoom tomogram recorded with waveguide illumination. voxel sizes: (i) 1.3µm, (ii) 650nm, (iii) 167nm. 3 datasets per sample, raw format, voxel numbers given in Filename, single (32bit real)
DOI: 10.5281/zenodo.3892636
title: 3d Virtual Patho-Histology of Lung Tissue from Covid19 Patients based on Phase Contrast X-ray Tomography

description: X-ray phase contrast tomographys datasets of lung tissue from 6 Covid-19 patients (paraffin embedded unstained tissue), and one control tissue (healthy lung, in fixative solution). The data has been recorded as part of the Covid-19 Call for Proposals at DESY April 2020 at the GINIX instrument, beamline P10 of the PETRAIII storage ring at 8keV. Three different datasets are included in a single tar archive for each sample: (i) overview scan (up to 4mm cross section ) from stitching different tomograms, (ii) 1mm cylindrical punch, and (iii) cone-beam zoom tomogram recorded with waveguide illumination. voxel sizes: (i) 1.3µm, (ii) 650nm, (iii) 167nm. 3 datasets per sample, raw format, voxel numbers given in Filename, single (32bit real)
DOI: 10.21227/efnn-0457
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. 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 these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.5281/zenodo.3900096
title: The Impact of liver disease on mortality in Cystic Fibrosis- A systematic review protocol-data

description: The data set to accompany a protocol for a systematic review entitled The impact of liver disease on mortality in cystic fibrosis Prisma P checklist (2015) Summary of Evidence table Risk of bias proforma
DOI: 10.5281/zenodo.3667534
title: Raw stable water isotope measurements in water vapour at 8 m a.s.l. on the starboard side of the ship, made in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition (ACE).

description: <strong>Dataset abstract</strong> This dataset includes the raw data of stable water vapour isotope (δ18O, δ2H, deuterium excess) and water vapour mixing ratio measurements at a height of approximately 8 meters above sea level taken in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE) from November 2016 to April 2017 using a Picarro laser spectrometer L2130-i mounted on the starboard side of the ship. L2130-i was operated only during two months in February and March 2017 from Hobart (Australia) to Cape Town (South Africa). This data has to be calibrated using the calibration runs and applying an appropriate calibration procedure following the IAEA guide lines. Two similar datasets also measuring stable water isotopes (SWIs) in water vapour were collected during the same expedition and can be distinguished from this dataset due to the height of the measurement collection (at 8 m a.s.l and 13.5 m a.s.l). The three ACE datasets of SWIs in water vapour are described and compared in Thurnherr et al. (2020). <strong>Dataset contents</strong> leg*/HIDS2018-yyyymmdd-hhmmssZ-DataLog_User.dat, data file, comma-separated values data_file_header.txt, metadata, text format README.txt, metadata, text format <strong>Dataset license</strong> This raw stable water isotope measurements dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.
description: {"references": ["Thurnherr, I., Kozachek, A., Graf, P., Weng, Y., Bolshiyanov, D., Landwehr, S. J., Pfahl, S., Schmale, J., Sodemann, H., Steen-Larsen, H. C., Toffoli, A., Wernli, H. and Aemisegger, F.: Meridional and vertical variations of the marine boundary layer water vapour isotope composition in the Atlantic and the Southern Ocean. Accepted (2020). Atmospheric Chemistry and Physics.", "IAEA: Reference Sheet for VSMOW2 and SLAP2 international measurement standards, International Atomic Energy Agency (IAEA), 2009", "David W H Walton, & Jenny Thomas. (2018, November 22). Cruise Report - Antarctic Circumnavigation Expedition (ACE) 20th December 2016 - 19th March 2017 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1443511"]}
DOI: 10.5281/zenodo.3888347
title: Spam: Software for Practical Analysis of Materials - datasets for tutorials

description: Datasets for spam tutorials: <strong>VEC4</strong> dataset from E.M. Charalampidou, E. Tudisco, S.A. Hall Experimental details available here: Charalampidou, E.M. (2011), "Experimental study of localised deformation in porous sandstones", <em>PhD Thesis</em> This dataset is two x-ray tomography scans of a notched sandstone sample scanned before and after deformation in an external rig.<br> There is a rigid body displacement between the two scans that can be taken into account nicely with a registration.<br> Scans from Laboratoire 3SR, Grenoble.<br> <strong>M2EA05</strong> dataset from E. Andò, G. Viggiani and J. Andrade.<br> This data already used in the following papers for comparison to numerical simulations: Kawamoto, R., Andò, E., Viggiani, G., & Andrade, J. E. (2016). Level set discrete element method for three-dimensional computations with triaxial case study. <em>Journal of the Mechanics and Physics of Solids</em>, <em>91</em>, 1-13 Nadimi, S., Fonseca, J., Andò, E., & Viggiani, G. (2019). A micro finite-element model for soil behaviour: experimental evaluation for sand under triaxial compression. <em>Géotechnique</em>, 1-6 This dataset is a series of x-ray tomographies acquired during the triaxial compression of a small sample of a "Martian Simulant" soil, performed in Laboratoire 3SR, Grenoble.<br> <strong>c01_F10_b4</strong> dataset from O. Stamati, E. Roubin, E. Andò and Y. Malecot <em>This dataset belongs to a paper which is under review</em> This is a 4-binned x-ray tomography of a small cylindrical sample of <em>micro-concrete</em><br> Scans from Laboratoire 3SR, Grenoble.<br> <strong>SandNX</strong> dataset from M. Milatz <em>The dataset is currently being analysed further</em> See this publication for the experimental setup: Milatz, M. (2020). An automated testing device for continuous measurement of the hysteretic water retention curve of granular media. <em>Acta Geotechnica</em>, 1-19<br> Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (Experiment UGA-73).<br> Neutron tomography has had a x0.7 downscale to bring it approximately in line with the pixel size for the x-ray tomography.<br> <strong>YehyaConcreteNX</strong> dataset from M. Yehya, A. Tengattini, E. Andò, F. Dufour Yehya, M., Ando, E., Dufour, F., & Tengattini, A. (2018). Fluid-flow measurements in low permeability media with high pressure gradients using neutron imaging: Application to concrete. <em>Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment</em>, <em>890</em>, 35-42 Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (Experiment UGA-25).
DOI: 10.5281/zenodo.3888346
title: Spam: Software for Practical Analysis of Materials - datasets for tutorials

description: Datasets for spam tutorials: <strong>VEC4</strong> dataset from E.M. Charalampidou, E. Tudisco, S.A. Hall Experimental details available here: Charalampidou, E.M. (2011), "Experimental study of localised deformation in porous sandstones", <em>PhD Thesis</em> This dataset is two x-ray tomography scans of a notched sandstone sample scanned before and after deformation in an external rig.<br> There is a rigid body displacement between the two scans that can be taken into account nicely with a registration.<br> Scans from Laboratoire 3SR, Grenoble.<br> <strong>M2EA05</strong> dataset from E. Andò, G. Viggiani and J. Andrade.<br> This data already used in the following papers for comparison to numerical simulations: Kawamoto, R., Andò, E., Viggiani, G., & Andrade, J. E. (2016). Level set discrete element method for three-dimensional computations with triaxial case study. <em>Journal of the Mechanics and Physics of Solids</em>, <em>91</em>, 1-13 Nadimi, S., Fonseca, J., Andò, E., & Viggiani, G. (2019). A micro finite-element model for soil behaviour: experimental evaluation for sand under triaxial compression. <em>Géotechnique</em>, 1-6 This dataset is a series of x-ray tomographies acquired during the triaxial compression of a small sample of a "Martian Simulant" soil, performed in Laboratoire 3SR, Grenoble.<br> <strong>c01_F10_b4</strong> dataset from O. Stamati, E. Roubin, E. Andò and Y. Malecot <em>This dataset belongs to a paper which is under review</em> This is a 4-binned x-ray tomography of a small cylindrical sample of <em>micro-concrete</em><br> Scans from Laboratoire 3SR, Grenoble.<br> <strong>SandNX</strong> dataset from M. Milatz <em>The dataset is currently being analysed further</em> See this publication for the experimental setup: Milatz, M. (2020). An automated testing device for continuous measurement of the hysteretic water retention curve of granular media. <em>Acta Geotechnica</em>, 1-19<br> Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (Experiment UGA-73).<br> Neutron tomography has had a x0.7 downscale to bring it approximately in line with the pixel size for the x-ray tomography.<br> <strong>YehyaConcreteNX</strong> dataset from M. Yehya, A. Tengattini, E. Andò, F. Dufour Yehya, M., Ando, E., Dufour, F., & Tengattini, A. (2018). Fluid-flow measurements in low permeability media with high pressure gradients using neutron imaging: Application to concrete. <em>Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment</em>, <em>890</em>, 35-42 Data acquired on NeXT-Grenoble, a simultaneous Neutron and X-ray scanner at the ILL in Grenoble (Experiment UGA-25).
DOI: 10.5281/zenodo.3895118
title: Dataset: A structural topic model approach to scientific reorientation of economics and chemistry after German reunification

description: Dataset to: A structural topic model approach to scientific reorientation of economics and chemistry after German reunification Please see readme.txt for details