cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/edh5-ay73
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.3902676
title: BIDMC Heart Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://timeseriesregression.org/ <br> The goal of this dataset is to estimate heart rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.3902716
title: Live Fuel Moisture Content Dataset

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://timeseriesregression.org/ The goal of this dataset is to predict the moisture content in vegetation. The moisture is the ratio between the weight of water in vegetation and the weight of the dry part of vegetation. This is known as the live fuel moisture content (LFMC) and is an important variable as the risk of fire increases very rapidly as soon as the LFMC goes below 80%.This data is paired with one year of daily reflectance data at 7 spectral bands (459 nm to 2155 nm) before the LFMC sampling date from the Moderate Resolution Imaging Spectrometer (MODIS) satellite. This dataset contains 5003 time series obtained from researchers at Monash University with 7 dimensions, each corresponding to one spectral band.<br> <br> Data Donor<br> Liujun Zhu, liujun.zhu@monash.edu
DOI: 10.5281/zenodo.3902648
title: eeg sleep

description: data
DOI: 10.5281/zenodo.3902647
title: eeg sleep

description: data
DOI: 10.5281/zenodo.3902688
title: BIDMC Blood Oxygen Saturation Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://timeseriesregression.org/ <br> The goal of this dataset is to estimate blood oxygen saturation level using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.3902685
title: BIDMC Respiratory Rate Dataset (32 seconds window)

description: This dataset is part of the Monash, UEA & UCR time series regression repository. http://timeseriesregression.org/ <br> The goal of this dataset is to estimate respiratory rate using PPG and ECG data. This dataset contains 7949 time series obtained from the Physionet's BIDMC PPG and Respiration dataset, which was extracted from the much larger MIMIC II waveform database.<br> <br> Please refer to https://physionet.org/content/bidmc/1.0.0/ for more details<br> <br> Relevant papers<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> <br> Citation request<br> Pimentel, M.A.F. et al. Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters. IEEE Transactions on Biomedical Engineering, 64(8), pp.1914-1923, 2016. [DOI: 10.1109/TBME.2016.2613124](http://doi.org/10.1109/TBME.2016.2613124).<br> Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
DOI: 10.5281/zenodo.3740410
title: Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

description: <pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
description: sources on https://gitlab.uliege.be/moammm/moammmpublic/tree/master/publicationsData/2020_CMAME_BI_NNW
DOI: 10.5281/zenodo.3740409
title: Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

description: <pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
description: sources on https://gitlab.uliege.be/moammm/moammmpublic/tree/master/publicationsData/2020_CMAME_BI_NNW
DOI: 10.21227/9qzq-0e69
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.3902737
title: Supporting data for "Powering the Galilean Satellites with Moon-Moon Tides'

description: Data files accompanying the submitted manuscript "Powering the Galilean Satellites with Moon-Moon Tides" for GRL.
DOI: 10.5281/zenodo.3902736
title: Supporting data for "Powering the Galilean Satellites with Moon-Moon Tides'

description: Data files accompanying the submitted manuscript "Powering the Galilean Satellites with Moon-Moon Tides" for GRL.
DOI: 10.21227/t9gn-y009
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.21227/sjn1-p821
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.5281/zenodo.3902550
title: Patent citation data for USPTO utility patents granted between 1976-2015 and for patents belonging to 30 technology domains

description: These two data file contains information on patent citations for USPTO utility patents granted between 1976 and 2015 and for patents that have been classified in 30 specific technology domains. The file 'CITATION_INFO_no_neg_citlag.csv' is generated combining raw data freely dowloadable from patentsview.org from which citations where the filing year of the citing patent is younger than the filing year of the cited one have been removed. The file 'CITATIONS_DOMAINS.csv' is a sample of the previous file that only includes citations made by patents belonging to one of 30 domains defined in the paper 'Estimating technology performance improvement rates by mining patent data' by Giorgio Triulzi, Jeff Alstott and Chris Magee. These two files complement another dataset published on Mendeley Data. The two datasets can be used, together with the code published on GitHub, to replicate the main results from the paper.
DOI: 10.5281/zenodo.3902549
title: Patent citation data for USPTO utility patents granted between 1976-2015 and for patents belonging to 30 technology domains

description: These two data file contains information on patent citations for USPTO utility patents granted between 1976 and 2015 and for patents that have been classified in 30 specific technology domains. The file 'CITATION_INFO_no_neg_citlag.csv' is generated combining raw data freely dowloadable from patentsview.org from which citations where the filing year of the citing patent is younger than the filing year of the cited one have been removed. The file 'CITATIONS_DOMAINS.csv' is a sample of the previous file that only includes citations made by patents belonging to one of 30 domains defined in the paper 'Estimating technology performance improvement rates by mining patent data' by Giorgio Triulzi, Jeff Alstott and Chris Magee. These two files complement another dataset published on Mendeley Data. The two datasets can be used, together with the code published on GitHub, to replicate the main results from the paper.
DOI: 10.18112/openneuro.ds002766.v3.0.0
title: Cast-induced plasticity
DOI: 10.17632/jn3kt83hzh.2
title: Experimental data on binary interactions of subaqueous barchans

description: This is part of the data concerning experiments on binary interactions of subaqueous barchans. In this dataset, you find animated movies and images for each pattern observed during lab experiments, and lists containing all tests performed on both configurations (aligned and off-centered position).
DOI: 10.17632/rsbp4cvszs.1
title: Paper SISI Dataset

description: This dataset contains data collected and analyzed for the elaboration of a paper to the discipline SISI of the doctoral program from the University of Coimbra.
DOI: 10.17632/j5b93k2xhk.1
title: Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques (Dataset and sample outputs)

description: This repository is the dataset of the related paper "Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques". The data presented here are collected and gathered together from several separate locations. All the probable original sources of the dataset are open-source or free to distribute licensed. The dataset has the following items: 1. Road network of Dhaka city. 2. Bus Route network of Dhaka city. 3. Future metro Route network of Dhaka city. 4. All the bus stands in Bangladesh. 5. All planned metro station in Dhaka city. 6. The output of some sample random two points shortest or cheapest path from the related paper.
DOI: 10.17632/x3mrhtxnby.1
title: Electrical resistivity survey datasets

description: This data consists of geological map, layout plan and geophysical survey lines in several location in Banggi Island, Sabah Malaysia. There are six inverse model resistivity data also provided here.
DOI: 10.17632/xm87k23hrh.1
title: Kudo et al, Cancer Cell 2020
DOI: 10.17632/c7gn2djxn8.1
title: An NAD+-dependent deacetylase SIRT7 promotes HCC development through deacetylation of USP39

description: raw data of the manuscript about western blotting
DOI: 10.17632/c7gn2djxn8.2
title: An NAD+-dependent deacetylase SIRT7 promotes HCC development through deacetylation of USP39

description: Raw data of the manuscript about western blotting
DOI: 10.17632/wj5wngc52s.2
title: Data for: The Role of Institutions in Environment-Health Outcomes Nexus: Empirical Evidence from Sub-Saharan Africa

description: lep Life expectancy at birth, total (years) lepf Life expectancy at birth, female (years) lepm Life expectancy at birth, male (years) mmat Maternal mortality ratio (modeled estimate, per 100,000 live births) minf Mortality rate, infant (per 1,000 live births) mun5 Mortality rate, under-5 (per 1,000 live births) hep Current health expenditure per capita, PPP (current international $) ghep Domestic general government health expenditure per capita, PPP (current international $) phep Domestic private health expenditure per capita, PPP (current international $) hout Health outcomes co2 Carbon emission kt per capita ecft Ecological footprint ccn Control of Corruption: Estimate ge Government Effectiveness: Estimate rqv Regulatory Quality: Estimate insq Institutional Quality
DOI: 10.17632/ht5s9cc845.1
title: Does time equal vision in the acute treatment of a cohort of AQP4 and MOG optic neuritis? MS ID#: NEURIMMINFL/ 2018/019398

description: In this study we tested the hypothesis that timing of intravenous (IVMP) affects visual outcome in a cohort of aquaporin4 (AQP4)-IgG and MOG-IgG positive patients with ON by analyzing the effect of the number of days until treatment commenced with the best-corrected visual acuity at three months. The results provides Class III evidence that hyperacute treatment of AQP4 and MOG-ON with IVMP increases the chance for excellent visual recovery (20/20 vision) and that even a greater than 7 day delay in treatment is associated with a higher risk for poor visual recovery.
DOI: 10.17632/5jw3dxhb87.1
title: Astrocytes Contribute to Remote Memory Formation by Modulating Hippocampal-Cortical Communication During Learning

description: Statistics source data (one file per main figure) for the Nature Neuroscience manuscript: "Astrocytes Contribute to Remote Memory Formation by Modulating Hippocampal-Cortical Communication During Learning".
DOI: 10.17632/79n74h842z.1
title: Data for: 3632241 Mutation Analytics Data

description: Mutation analytics and mutation data on SARS-COV-2 genomic sequences. Comprehensive analysis of mutagenesis.
DOI: 10.17632/538s8bzz99.1
title: Survey Data Responses on Employee Happiness: Focusing On Individual Factor Among Staff In Malaysian Higher Education Institution

description: This data presented the descriptive data on organization factors that contribute to staff happiness. The survey was conducted in one of Malaysian Higher Education Institution which is located at southern of the Malay Peninsula. The study involved 15 respondents comprising academic and non-academic staff at Universiti Tun Hussein Onn Malaysia. The session of Round Table Discussion was organised to discuss the issues has been highlighted from survey data. Discussion topic covered the opinion and suggestion of respondent to what are the personal factors proposed are contribute to staff happiness. The Round Table Discussion sessions were audio-recorded and transcribed. Informants were given a copy of the transcripts to review in ensuring accuracy. The resulting study data was then analyzed using thematic analysis. To ensure data trustworthiness, the researcher applied both participant and literature review cross-checks. Data analyses shows that the personal economy and appreciation dimension were the highest contributors in determining the employee happiness followed by support system, work station environment and trust. As a conclusion, the data set provides an initial record of how personal factor of employee in the organisation affect their happiness at work place.
DOI: 10.17632/6dmm8659rm.3
title: Dataset of tandem mass spectra for non-halal pork-based food products
DOI: 10.17632/zdgtky97cz.5
title: Critical rare earth elements mismatch global wind power ambitions

description: The raw data and code for calculating rare earth demand under four wind power scenarios.
DOI: 10.17632/4cpvmbcj43.1
title: Raw data GEO OMNIBUS 19ID00315-318

description: Results from genomic stability assays of NKG2D-CAR transduced activated and expanded natural killer (NKAE) cells and memory T cells from multiple myeloma patients.
DOI: 10.17632/m79ygj3rwr.1
title: Time-lapse microscopy of CAR-NKAE cells and memory CAR-T cells from multiple myeloma patients

description: Myeloma cells from U-266 cell line (green fluorescent cells) were seeded forming a monolayer on a six-channel polymer-treated flow chamber. Then a constant flux of either CAR-NKAE cells (red fluorescent cells, left) or memory CAR-T cells (red fluorescent cells, right) from the same MM patient was established over 20 min. Subsequently, four-hour observation of CAR-NKAE cell and memory CAR-T cell activity was performed.
DOI: 10.17632/tmrs92j7pv.13
title: COVID-19: Time Series Datasets India versus World

description: This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]