cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/01ww-1549
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 18,398,713This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets, using filters: language “en” and keyword “corona”. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose (ignore every other LICENSE category given in this page).Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details (Tweets collected in GMT+0; Local time mentioned below: GMT+5:45):corona_tweets_01.csv: 831,327 tweets (March 20, 2020 01:37 AM - March 20, 2020 10:28 AM)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31 AM - March 20, 2020 09:43 PM)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 09:49 PM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35 AM)corona_tweets_07.csv: 1,479,651 tweets (March 24, 2020 11:42 AM - March 25, 2020 11:43 AM)corona_tweets_08.csv: 1,272,592 tweets (March 25, 2020 11:47 AM - March 26, 2020 12:46 PM)corona_tweets_09.csv: 1,091,429 tweets (March 26, 2020 12:51 PM - March 27, 2020 11:53 AM)corona_tweets_10.csv: 1,172,013 tweets (March 27, 2020 11:56 AM - March 28, 2020 01:59 PM)corona_tweets_11.csv: 1,141,210 tweets (March 28, 2020 02:03 PM - March 29, 2020 04:01 PM)----- March 29, 2020 04:05 PM - March 30, 2020 12:30 PM -- Some folk(s) messed around with the server. Tweets for this period won't be available. However, I'll be continuing adding the new Tweet IDs. Some preventive measures have been taken. Sorry for the inconvenience. -----corona_tweets_12.csv: 793,417 tweets. (March 30, 2020 02:01 PM - March 31, 2020 10:16 AM)corona_tweets_13.csv: 1,029,294 tweets (March 31, 2020 10:20 AM - April 01, 2020 10:59 AM)corona_tweets_14.csv: 920,076 tweets (April 01, 2020 11:02 AM - April 02, 2020 12:19 PM)corona_tweets_15.csv: 826,271 tweets (April 02, 2020 12:21 PM - April 03, 2020 02:38 PM)corona_tweets_16.csv: 612,512 tweets (April 03, 2020 02:40 PM - April 04, 2020 11:54 AM)corona_tweets_17.csv: 685,560 tweets (April 04, 2020 11:56 AM - April 05, 2020 12:54 PM)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.3886/e114014v1-23523
title: Replication data for: Microeconomic Heterogeneity and Macroeconomic Shocks

description: In this essay, we discuss the emerging literature in macroeconomics that combines heterogeneous agent models, nominal rigidities, and aggregate shocks. This literature opens the door to the analysis of distributional issues, economic fluctuations, and stabilization policies—all within the same framework. In response to the limitations of the representative agent approach to economic fluctuations, a new framework has emerged that combines key features of heterogeneous agents (HA) and New Keynesian (NK) economies. These HANK models offer a much more accurate representation of household consumption behavior and can generate realistic distributions of income, wealth, and, albeit to a lesser degree, household balance sheets. At the same time, they can accommodate many sources of macroeconomic fluctuations, including those driven by aggregate demand. In sum, they provide a rich theoretical framework for quantitative analysis of the interaction between cross-sectional distributions and aggregate dynamics. In this article, we outline a state-of-the-art version of HANK together with its representative agent counterpart, and convey two broad messages about the role of household heterogeneity for the response of the macroeconomy to aggregate shocks: 1) the similarity between the Representative Agent New Keynesian (RANK) and HANK frameworks depends crucially on the shock being analyzed; and 2) certain important macroeconomic questions concerning economic fluctuations can only be addressed within heterogeneous agent models.
DOI: 10.5281/zenodo.3740577
title: Massive donors in interacting binaries: impact of metallicity

description: Input files needed to reproduce the simulations as well as output MESA history files (i.e. stellar tracks) to paper "Massive donors in interacting binaries: impact of metallicity". MESA version r11554. Output history files are provided for the models with the chosen reference set of input physical assumptions. History files from other models discussed in the paper can be provided upon request.
DOI: 10.5281/zenodo.3740578
title: Massive donors in interacting binaries: impact of metallicity

description: Input files needed to reproduce the simulations as well as output MESA history files (i.e. stellar tracks) to paper "Massive donors in interacting binaries: impact of metallicity". MESA version r11554. Output history files are provided for the models with the chosen reference set of input physical assumptions. History files from other models discussed in the paper can be provided upon request.
DOI: 10.7910/dvn/rtazzg/0t6g3o
title: geocode_result_04.zip

description: Atualizado 2020/04/01
DOI: 10.3886/e114097v1-23525
title: Replication data for: Optimal Monetary and Prudential Policies

description: The recent financial crisis has highlighted the interconnectedness between macroeconomic and financial stability, raising questions about how to combine monetary and prudential policies. This paper characterizes the jointly optimal monetary and prudential policies, setting the interest rate and bank-capital requirements. The source of financial fragility is the socially excessive risk taking by banks due to limited liability and deposit insurance. We provide conditions under which locally (Ramsey) optimal policy dedicates the prudential instrument to preventing inefficient risk taking by banks, and the monetary instrument to dealing with the business cycle, with the two instruments covarying either negatively, or positively and countercyclically.
DOI: 10.25345/c54q34
title: MassIVE MSV000085230 - Automated coupling of nanodroplet sample preparation with liquid chromatography-mass spectrometry for high throughput single-cell proteomics
title: Development and evaluation of an autosampler for integrating nanoPOTS with LC-MS. Includes proteomic data from nanoPOTS-based sample preparation, including diluted peptides of Shewanella oneidensis MR-1, single cultured MCF10A cells, and single cells from three Acute Myeloid Leukemia (AML) cell lines (MOLM-14, K562, and CMK). Data was searched with MaxQuant.
DOI: 10.3886/e114136v1-23527
title: Replication data for: A Model of Endogenous Loan Quality and the Collapse of the Shadow Banking System

description: I develop a macroeconomic model in which banks can affect loan quality by exerting costly screening effort. Informational frictions limit the amount of external funds that banks can raise. In this framework I consider two types of financial intermediation, traditional banking and shadow banking. By pooling different loans, shadow banks achieve a higher endogenous leverage compared to traditional banks, increasing credit availability. However, shadow banks also make the financial sector more fragile, because of the lower quality of the loans they finance and because of their exposure to bank runs. In this setting unconventional monetary policy can reduce macroeconomic instability.
DOI: 10.6084/m9.figshare.3761562.v1442
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1443
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.7910/dvn/k8e5qr/fhyfdt
title: COVID-19_Med_Chem.doc

description: Article.
DOI: 10.7910/dvn/k8e5qr/hc5qea
title: COVID-19_Med_Chem.pdf

description: Article.
DOI: 10.21227/df9a-d046
title: ship motion

description: The ship motion data collected in this paper is actually measured when the ship is sailing at sea. The heave displacement is measured by a laser ranging sensor. The roll and pitch angles are collected by an electronic inclinometer. The sampling time of the data set exceeds 1000s, the collection interval is 0.176s, the collection frequency is 6 times per second, and there are more than 6000 sets of data in total.
DOI: 10.21227/sk98-8j20
title: ship motion

description: The ship motion data collected in this paper is actually measured when the ship is sailing at sea. The heave displacement is measured by a laser ranging sensor. The roll and pitch angles are collected by an electronic inclinometer. The sampling time of the data set exceeds 1000s, the collection interval is 0.176s, the collection frequency is 6 times per second, and there are more than 6000 sets of data in total.
DOI: 10.5281/zenodo.3740414
title: Natural variation across Arabidopsis diversity panel in early responses to salt stress

description: The data of Arabidopsis thaliana accessions which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic.
DOI: 10.5281/zenodo.3740415
title: Natural variation across Arabidopsis diversity panel in early responses to salt stress

description: The data of Arabidopsis thaliana accessions which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic.
DOI: 10.5281/zenodo.3740622
title: IO Islamic 3229. Tawârîkh-i-Mulk-i-Hazâra, History of the State of Hazâra and the Neighboring States and Districts in Panjâb

description: IO Islamic 3229. Tawârîkh-i-Mulk-i-Hazâra, History of the State of Hazâra and the Neighboring States and Districts in Panjâb
DOI: 10.5281/zenodo.3740623
title: IO Islamic 3229. Tawârîkh-i-Mulk-i-Hazâra, History of the State of Hazâra and the Neighboring States and Districts in Panjâb

description: IO Islamic 3229. Tawârîkh-i-Mulk-i-Hazâra, History of the State of Hazâra and the Neighboring States and Districts in Panjâb
DOI: 10.5519/qd.belawhav
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.3740428
title: Spatially corrected dataset for natural variation across Arabidopsis diversity panel in early responses to salt stress

description: The spatially corrected data of Arabidopsis thaliana accessions, which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic. The spatial correction was done using the asreml package. The data was subsequently used for the Genome-Wide Association Study.
description: The non-spatially corrected data is available at https://doi.org/10.5281/zenodo.3740415
DOI: 10.5281/zenodo.3740429
title: Spatially corrected dataset for natural variation across Arabidopsis diversity panel in early responses to salt stress

description: The spatially corrected data of Arabidopsis thaliana accessions, which were grown according to the established protocol for studying salt stress in soil experiment (described in detail here dx.doi.org/10.17504/protocols.io.4xzgxp6), in the PSI facility, Czech Republic. The spatial correction was done using the asreml package. The data was subsequently used for the Genome-Wide Association Study.
description: The non-spatially corrected data is available at https://doi.org/10.5281/zenodo.3740415
DOI: 10.5281/zenodo.3740637
title: IO Islamic 3181 (2). Tawârîkh-i-Râjagân-i-Jamûn, A Portion of the Rare Chronicle of the Râjahs of Jamûn

description: IO Islamic 3181 (2). Tawârîkh-i-Râjagân-i-Jamûn, A Portion of the Rare Chronicle of the Râjahs of Jamûn
DOI: 10.5281/zenodo.3740638
title: IO Islamic 3181 (2). Tawârîkh-i-Râjagân-i-Jamûn, A Portion of the Rare Chronicle of the Râjahs of Jamûn

description: IO Islamic 3181 (2). Tawârîkh-i-Râjagân-i-Jamûn, A Portion of the Rare Chronicle of the Râjahs of Jamûn
DOI: 10.5281/zenodo.3740644
title: Precio_Medio_Combustible_Municipios_ES

description: El dataset esta formado por los precios medios de cada tipo de combustible fósil, gasolina, diésel, etc… de cada uno de los municipios de cada ciudad española. En este dataset, encontraremos nombre de provincia, nombre de municipio, fecha de obtención de los datos de cada registro, así como cada uno de los tipos de gasolina con su precio, en caso de disponer de dicho tipo de combustible en el municipio localizado. Campos: City: Provincia española Town: Municipio español. gasolina 95: Precio €/l del Tipo combustible Gasolina 95 gasolina 98: Precio €/l del Tipo combustible Gasolina 98 diésel: Precio €/l del Tipo combustible diésel diésel plus: Precio €/l del Tipo combustible diésel plus gasóleo B: Precio €/l del Tipo combustible gasóleo B biodiesel: Precio €/l del Tipo combustible biodiesel GNL: Precio €/l del Tipo combustible GNL GNC: Precio €/l del Tipo combustible GNC GLP: Precio €/l del Tipo combustible GLP Fecha: Fecha de obtención de los datos
DOI: 10.5281/zenodo.3740645
title: Precio_Medio_Combustible_Municipios_ES

description: El dataset esta formado por los precios medios de cada tipo de combustible fósil, gasolina, diésel, etc… de cada uno de los municipios de cada ciudad española. En este dataset, encontraremos nombre de provincia, nombre de municipio, fecha de obtención de los datos de cada registro, así como cada uno de los tipos de gasolina con su precio, en caso de disponer de dicho tipo de combustible en el municipio localizado. Campos: City: Provincia española Town: Municipio español. gasolina 95: Precio €/l del Tipo combustible Gasolina 95 gasolina 98: Precio €/l del Tipo combustible Gasolina 98 diésel: Precio €/l del Tipo combustible diésel diésel plus: Precio €/l del Tipo combustible diésel plus gasóleo B: Precio €/l del Tipo combustible gasóleo B biodiesel: Precio €/l del Tipo combustible biodiesel GNL: Precio €/l del Tipo combustible GNL GNC: Precio €/l del Tipo combustible GNC GLP: Precio €/l del Tipo combustible GLP Fecha: Fecha de obtención de los datos
DOI: 10.21227/jkyd-fs94
title: Motion-Print Control Task Movements (38 subjects)

description: This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. Both raw data and preprocessed data folders are included.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained in one file. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate, aggregation is performed in preprocessing, to produce the files in the 'subjects preprocessed (Train/Test split)' folder. Depending on the chosen aggregation rate, each run at 100 HZ is sampled down to 50, 20, 10 or 5 HZ, by combining every 50, 20, 10 or 5 raw data points into one respectively. This reduces the number of rows by a factor of 50, 20 10 or 5, thus reducing the size of the data files by the same factor. The preprocessed data files found in 'subjects preprocessed (Train/Test split)' was aggregated down to 5 HZ.In the 'subjects preprocessed (Train/Test split)' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. The 'TRAIN' files are larger, containing the subjects' movements on 12 runs of the task. The 'TEST' files are smaller, containing the subjects' movements on the remaining 3 runs of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.
DOI: 10.21227/wpyf-r927
title: Motion-Print Control Task Movements (38 subjects)

description: This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. Both raw data and preprocessed data folders are included.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained in one file. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate, aggregation is performed in preprocessing, to produce the files in the 'subjects preprocessed (Train/Test split)' folder. Depending on the chosen aggregation rate, each run at 100 HZ is sampled down to 50, 20, 10 or 5 HZ, by combining every 50, 20, 10 or 5 raw data points into one respectively. This reduces the number of rows by a factor of 50, 20 10 or 5, thus reducing the size of the data files by the same factor. The preprocessed data files found in 'subjects preprocessed (Train/Test split)' was aggregated down to 5 HZ.In the 'subjects preprocessed (Train/Test split)' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. The 'TRAIN' files are larger, containing the subjects' movements on 12 runs of the task. The 'TEST' files are smaller, containing the subjects' movements on the remaining 3 runs of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.
DOI: 10.5519/qd.k0jacts5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1749 records)
DOI: 10.21227/6qeg-9h73
title: Motion-Print Control Task Movements (38 subjects)

description: This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. Both raw data and preprocessed data folders are included.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained in one file. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate, aggregation is performed in preprocessing, to produce the files in the 'subjects preprocessed (Train/Test split)' folder. Depending on the chosen aggregation rate, each run at 100 HZ is sampled down to 50, 20, 10 or 5 HZ, by combining every 50, 20, 10 or 5 raw data points into one respectively. This reduces the number of rows by a factor of 50, 20 10 or 5, thus reducing the size of the data files by the same factor. The preprocessed data files found in 'subjects preprocessed (Train/Test split)' was aggregated down to 5 HZ.In the 'subjects preprocessed (Train/Test split)' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. The 'TRAIN' files are larger, containing the subjects' movements on 12 runs of the task. The 'TEST' files are smaller, containing the subjects' movements on the remaining 3 runs of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.
DOI: 10.21227/93az-nt21
title: Motion-Print Control Task Movements (38 subjects)

description: This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. Both raw data and preprocessed data folders are included.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained in one file. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate, aggregation is performed in preprocessing, to produce the files in the 'subjects preprocessed (Train/Test split)' folder. Depending on the chosen aggregation rate, each run at 100 HZ is sampled down to 50, 20, 10 or 5 HZ, by combining every 50, 20, 10 or 5 raw data points into one respectively. This reduces the number of rows by a factor of 50, 20 10 or 5, thus reducing the size of the data files by the same factor. The preprocessed data files found in 'subjects preprocessed (Train/Test split)' was aggregated down to 5 HZ.In the 'subjects preprocessed (Train/Test split)' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. The 'TRAIN' files are larger, containing the subjects' movements on 12 runs of the task. The 'TEST' files are smaller, containing the subjects' movements on the remaining 3 runs of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.
DOI: 10.25919/5e8927a706b34
title: Parkes observations for project PX501 semester 2019OCTS_11

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX500) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first (PX500) will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time (relating to this proposal) will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.21227/5849-qr51
title: Motion-Print Control Task Movements (38 subjects)

description: This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. Both raw data and preprocessed data folders are included.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained in one file. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate, aggregation is performed in preprocessing, to produce the files in the 'subjects preprocessed (Train/Test split)' folder. Depending on the chosen aggregation rate, each run at 100 HZ is sampled down to 50, 20, 10 or 5 HZ, by combining every 50, 20, 10 or 5 raw data points into one respectively. This reduces the number of rows by a factor of 50, 20 10 or 5, thus reducing the size of the data files by the same factor. The preprocessed data files found in 'subjects preprocessed (Train/Test split)' was aggregated down to 5 HZ.In the 'subjects preprocessed (Train/Test split)' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. The 'TRAIN' files are larger, containing the subjects' movements on 12 runs of the task. The 'TEST' files are smaller, containing the subjects' movements on the remaining 3 runs of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.