cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3899941
title: Lagrangian particle dataset (2 years) for Agulhas region surface flow

description: This is a .npz file containing the longitudes and latitudes of approximately 24,000 virtual Lagrangian particles (0.2 degree in longitude and latitude initial spacing) released on January 5, 2000 in the eastern South Atlantic Ocean and advected for 2 years. The data was used for the detection of finite-time coherent sets (Agulhas rings), see this github repository for the usage and this github repository for simulation details. Trajectories were simulated using OceanParcels (https://oceanparcels.org/), with surface currents derived from a NEMO-ORCA 006 run, available at http://opendap4gws.jasmin.ac.uk/thredds/nemo/root/catalog.html.
DOI: 10.5519/qd.rrbhhs2h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.obpjs8lu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (102 records)
DOI: 10.7910/dvn/scsquf/wyy4wo
title: Readme.txt

description: :unav
DOI: 10.7910/dvn/scsquf/fhlhi2
title: Tesouro_Direto-Prices_and_Yields.tab

description: :unav
DOI: 10.25430/researchdata.cab.unipd.it.00000358
title: Evidence for complex iron oxides in the deep mantle from FeNi(Cu) inclusions in super-deep diamond

description: Electron microprobe analyses (wt.%) of ferropericlase inclusion AZ1_1 (39 spots).
DOI: 10.21227/9frn-7208
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/bbh9-7z19
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.5281/zenodo.3899468
title: The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel

description: This is the R script, full data and data for figures 3 and 4 of the article "The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel"
DOI: 10.5281/zenodo.3899467
title: The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel

description: This is the R script, full data and data for figures 3 and 4 of the article "The Effect of Vaccination with Live Attenuated Neethling Lumpy Skin Disease Vaccine on Milk Production and Mortality—an Analysis of 77 Dairy Farms in Israel"
DOI: 10.5281/zenodo.3900075
title: 02_Pegel_Abfluss_OR

description: Die Pegel und Abflussdaten von den Schlüsselkurven der optimalen Referenz (OR)
DOI: 10.5281/zenodo.3900076
title: 02_Pegel_Abfluss_OR

description: Die Pegel und Abflussdaten von den Schlüsselkurven der optimalen Referenz (OR)
DOI: 10.5281/zenodo.3900082
title: 03_Gerinnequerprofile

description: Die Querprofile der fünf Messstandorte in der Falllinie der Pegeldrucksonden
DOI: 10.5281/zenodo.3900081
title: 03_Gerinnequerprofile

description: Die Querprofile der fünf Messstandorte in der Falllinie der Pegeldrucksonden
DOI: 10.5285/a2104adc-e98e-6789-e053-6c86abc0d557
title: Abundance of microbial phytoplankton through the water column during the AMT25 (JR15001) cruise in September-November 2015.

description: This dataset contains depth profiles of the mean abundance of cyanobacteria and small (&lt;20 um) phytoplankton reported in cells per millilitre. Samples were measured using flow cytometry during the Atlantic Meridional Transect (AMT) cruise AMT25 (JR15001). The samples were collected between September and November 2015 from 200 m to the surface, using a Seabird CTD system with a rosette of 24 Niskin bottles of 20 L volume. Samples were collected from the predawn and noon CTDs and were stored in a refrigerator and analysed within 3 hours of collection. In total 915 discrete samples were analysed and quantified using a Becton Dickinson FACSort flow cytometer which characterised and enumerated <em>Prochlorococcus</em> sp. and <em>Synechococcus</em> sp. (cyanobacteria) and pico- and eukaryotic phytoplankton, based on their light scattering and autofluorescence properties. Data were analysed onboard the ship during the cruise.
DOI: 10.5519/qd.esa3odvq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (83 records)
DOI: 10.5519/qd.xi3o8k9i
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (607 records)
DOI: 10.5281/zenodo.3900092
title: 05_2D_Modell_BASEMENT

description: Darin enthalten sind die "run.bmc" Command files, der Hydrograph, die strindDefs und die Gerinnegeometrie für jeden der fünf Standorte
DOI: 10.5281/zenodo.3900091
title: 05_2D_Modell_BASEMENT

description: Darin enthalten sind die "run.bmc" Command files, der Hydrograph, die strindDefs und die Gerinnegeometrie für jeden der fünf Standorte
DOI: 10.5519/qd.8itx6kir
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (108 records)
DOI: 10.5519/qd.46d4r238
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (127 records)
DOI: 10.5519/qd.ggth0kv3
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (664 records)
DOI: 10.5519/qd.yp7bfahg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2625 records)
DOI: 10.5281/zenodo.3900086
title: 04_1D_Modell_Beispielinput

description: Das Python Kernskript des 1D Modells und Beispiel-Inputdaten (txt.) für das 1D Modell/Skript
DOI: 10.5281/zenodo.3900085
title: 04_1D_Modell_Beispielinput

description: Das Python Kernskript des 1D Modells und Beispiel-Inputdaten (txt.) für das 1D Modell/Skript
DOI: 10.5258/soton/d1426
title: The Secure Remote Update Protocol: A Seccification (v2.1)

description: Dataset supports: Poulter, A et al (2017) Extensions and Enhancements to 'The Secure Remote Update Protocol’ (SRUP). Future Internet. This document lays out a complete description of the message types, and their data format for use with the Secure Remote Update Protocol. This is version 2.1 of the SRUP Specification document.
DOI: 10.5281/zenodo.3766220
title: The zebra mussel (Dreissena polymorpha) as a model organism for ecotoxicological studies: a prior 1H NMR spectrum interpretation of a whole body extract for metabolism monitoring.

description: NMR data of the zebra mussel <em>Dreissena polymorpha</em> whole body polar extract metabolome - 1D <sup>1</sup>H annotated spectrum - 600 MHz - 2D <sup>1</sup>H-<sup>1</sup>H JRES spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>1</sup>H COSY spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>1</sup>H TOCSY spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 800 MHz - 2D<sup>1</sup>H-<sup>31</sup>P HSQC spectrum - 800 MHz - <sup>1</sup>H annotated spectrum description tables (.xlsx) - Instructions for data visualization in Topspin
description: {"references": ["Prud'homme, S.M.; Hani, Y.M.I.; Cox, N.; Lippens, G.; Nuzillard, J.-M.; Geffard, A. The Zebra Mussel (Dreissena polymorpha) as a Model Organism for Ecotoxicological Studies: A Prior 1H NMR Spectrum Interpretation of a Whole Body Extract for Metabolism Monitoring. Metabolites 2020, 10, 256."]}
DOI: 10.5281/zenodo.3766219
title: The zebra mussel (Dreissena polymorpha) as a model organism for ecotoxicological studies: a prior 1H NMR spectrum interpretation of a whole body extract for metabolism monitoring.

description: NMR data of the zebra mussel <em>Dreissena polymorpha</em> whole body polar extract metabolome - 1D <sup>1</sup>H annotated spectrum - 600 MHz - 2D <sup>1</sup>H-<sup>1</sup>H JRES spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>1</sup>H COSY spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>1</sup>H TOCSY spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 600 MHz - 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 800 MHz - 2D<sup>1</sup>H-<sup>31</sup>P HSQC spectrum - 800 MHz - <sup>1</sup>H annotated spectrum description tables (.xlsx) - Instructions for data visualization in Topspin
description: {"references": ["Prud'homme, S.M.; Hani, Y.M.I.; Cox, N.; Lippens, G.; Nuzillard, J.-M.; Geffard, A. The Zebra Mussel (Dreissena polymorpha) as a Model Organism for Ecotoxicological Studies: A Prior 1H NMR Spectrum Interpretation of a Whole Body Extract for Metabolism Monitoring. Metabolites 2020, 10, 256."]}
DOI: 10.5258/soton/d1240
title: Alderney Race TELEMAC2D numerical model input files

description: This dataset supports: Blunden, Haynes and Bahaj (2020) "Tidal current power effects on nearby sandbanks: a case study in the Race of Alderney", Trans. Roy. Soc. Pt. A. Please cite this paper in any publications emanating from the use of this data. Boundary conditions, configuration and fortran files relating to the simulations of sediment transport under different tidal power scenarios in Alderney waters, using TELEMAC2D V6.1 open source hydrodynamic software (http://opentelemac.com). See readme.txt in top directory of the untarred file for details of how to run the files.
DOI: 10.5281/zenodo.3899933
title: HOMFAM CLE FGP

description: HOMFAM dataset and scripts for FGP of Carlos López Elorduy
DOI: 10.5281/zenodo.3899932
title: HOMFAM CLE FGP

description: HOMFAM dataset and scripts for FGP of Carlos López Elorduy
DOI: 10.21227/r5pd-vc98
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/dwh3-jk65
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I've started hydrating the IDs myself, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task gets complete. I'll be adding new CSV files and the naming convention for these newly added files with be day-wise (instead of period wise). Bookmark this page for further updates.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]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-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.21227/w311-tc94
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I've started hydrating the IDs myself, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task gets complete. I'll be adding new CSV files and the naming convention for these newly added files with be day-wise (instead of period wise). Bookmark this page for further updates.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-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.21227/215n-2p97
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I've started hydrating the IDs myself, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task gets complete. I'll be adding new CSV files and the naming convention for these newly added files with be day-wise (instead of period-wise). Bookmark this page for further updates.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-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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: 10.21227/1vrc-xs88
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(190+ 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 make available the geo-tagged tweets created between March 20, 2020, and April 28, 2020. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I've started hydrating the IDs myself, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task gets complete. I'll be adding new CSV files and the naming convention for these newly added files will be day-wise (instead of period-wise). Bookmark this page for further updates.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-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.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.