cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/7jkn-yc53
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user Ids. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.21227/edc6-q229
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.
DOI: 10.21227/mmh0-8c69
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.
DOI: 10.21227/xe5g-n920
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.
DOI: 10.25345/c5612g
title: MassIVE MSV000085504 - Synergy between T6SSs is an important component of X. nematophila infection of the insect host
title: The type VI secretion system (T6SS) acts as a molecular weapon, delivering toxic effector molecules into prey cells. Agar plate-based experiments indicate that these systems function in bacterial competition, but T6SS function in ecologically relevant settings is less well known. The gram-negative bacterium Xenorhabdus nematophila and its nematode host Steinernema carpocapsae, engage in a mutualistic relationship and together parasitize insects. Inside the insect, X. nematophila is a pathogen and must overcome immune defenses present in the insect blood system. Here, we assessed the role of two T6SSs, T6SS-1 and T6SS-2, in X. nematophila ATCC 19061 during interaction with the agricultural insect pest, Manduca sexta. We discovered that in X. nematophila both T6SSs are necessary for normal cytolytic activity against M. sexta immune cells. The T6SS-2 deletion mutant had attenuated virulence as well as overactivation of the prophenoloxidase (proPO) immune cascade. These T6SS-2 mutant virulence and proPO suppression defects were rescued when the T6SS-1 locus was also deleted, suggesting an unexpected coordination of activity between the two systems. We searched for possible T6SS effectors of host interaction using a proteomic and a comparative genomic analysis. The former discovered possible novel T6SS effectors and suggests a role of X. nematophila T6SS in redox homeostasis. The genomic analysis revealed widespread conservation of T6SSs across the Xenorhabdus genus and significant variation in number of T6SS clusters and effector chaperones at both the species and strain levels. Our study reveals new links between T6SS, immune system interactions, and cluster coordination in the Xenorhabdus lifecycle.
DOI: 10.21227/60me-5406
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user Ids. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.21227/8t0k-xa91
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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 with an exact location that contained any of the previously mentioned keywords.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user Ids. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.21227/khkq-d905
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's 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.Note: I started sharing the IDs of the tweets that contained the exact 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 (above 120+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.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-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.25345/c58t55
title: MassIVE MSV000085513 - GNPS MMS1589 F1 RG/SB Fractionnement MZmine processed
title: Fractionnation dataset pre processes throught MZmine workflow for dereplication and assessement of chemical diversity
DOI: 10.25919/5ed267c9930a2
title: Parkes observations for project P456 semester 2020APRS_03

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. The PPTA is the oldest of three international pulsar timing groups. We have the smallest telescope and the smallest group, but the best measurements and the best results. Our bound on the strength of the incoherent gravitational wave background is the only bound which significantly constrains theoretical models. We have maintained our leading position because: we have the southern sky; we have a small but well-focussed group; we have the best-calibrated receivers; and we have been able to observe with an almost regular cadence of 15 to 20 days over a wide bandwidth. With this proposal we aim to maintain our pre-eminent position in the field. Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.3886/e119686
title: Does Real Estate Price Affect Corporate Innovation in China?

description: The relationship between Chinese real estate prices and enterprises’ innovation is surveyed in the research. Real estate can be divided into residential real estate and commercial one in China. An empirical analysis of the data of Chinese listed companies in 2007-2018 shows that: (1) real estate prices are negatively related to corporate innovation, and (2) investment crowding-out theory and financing constraint theory cannot explain the phenomenon, which is explained by mental accounting theory, instead. In addition, with the rise-up of real estate prices, corporate innovation decreases in a short term, but rises for compensation after 1-3 years, while returning to normal level in 5 years. Finally, it is found that enterprises are more sensitive to commercial real estate prices than the price of residential one. The main contributions of this paper lie in identification of the above two major theories not suitable for Chinese enterprises and suggestion of new explanations. Future research, therefore, should avoid using investment crowding-out theory and financing constraints theory when investigating relevant Chinese topics.
DOI: 10.3886/e119686v1
title: Does Real Estate Price Affect Corporate Innovation in China?

description: The relationship between Chinese real estate prices and enterprises’ innovation is surveyed in the research. Real estate can be divided into residential real estate and commercial one in China. An empirical analysis of the data of Chinese listed companies in 2007-2018 shows that: (1) real estate prices are negatively related to corporate innovation, and (2) investment crowding-out theory and financing constraint theory cannot explain the phenomenon, which is explained by mental accounting theory, instead. In addition, with the rise-up of real estate prices, corporate innovation decreases in a short term, but rises for compensation after 1-3 years, while returning to normal level in 5 years. Finally, it is found that enterprises are more sensitive to commercial real estate prices than the price of residential one. The main contributions of this paper lie in identification of the above two major theories not suitable for Chinese enterprises and suggestion of new explanations. Future research, therefore, should avoid using investment crowding-out theory and financing constraints theory when investigating relevant Chinese topics.
DOI: 10.5519/qd.txi8v8l3
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (370 records)
DOI: 10.25345/c5p11f
title: MassIVE MSV000085508 - GNPS - Undiagnosed Disease Network Study Results, Plasma lipidomics and metabolomics
title: The deposited data were collected from 148 patients and 133 family members accepted into the Undiagnosed Diseases Network (https://undiagnosed.hms.harvard.edu/). The NIH Common Fund Undiagnosed Diseases Network (UDN) seeks to provide diagnoses for individuals with undiagnosed disease. Here, we report and provide the mass spectrometry-based metabolomics (GC-MS) and lipidomics (LC-MS/MS) analyses of blood plasma from 148 patients and 133 family members. We have deposited mass spectrometry-based metabolomics and lipidomics files including instrument files, normalized data processed files to allow for statistical analysis, and metabolomics and lipidomics results for each patient and associated relatives. In addition, as part of the mass spectrometry data made available, we have included mass spectrometry analyses and results from a reference population of individuals with no known metabolic diseases. UDN patients suffer from undiagnosed diseases and thus are typically represented as a sample size of one; therefore, understanding normal variation within a proband's condition needs to be measured against of dataset of normal individuals, which is included here.
DOI: 10.5066/p9c68h73
title: Supporting data for Hanalei Watershed model: SWAT_Hanalei

description: This data release contains inputs and outputs needed to reproduce the Soil and Water Assessment Tool (SWAT) model findings for the publication: Fortini, L.B., Leopold, C.R., Perkins, K., Chadwick, O.A., Yelenik, S.G., Jacobi, J.D., Bishaw, K., II, Gregg, M. and Rosa, S., 2020, Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients, University of Hawai'i at Hilo Hawai`i Cooperative Studies Unit Technical Report HCSU-TR095, 93p., http://hdl.handle.net/10790/5282. The SWAT model, a physically based, semi-distributed, continuous model, was used to develop a watershed model for Hanalei, Kaua?i (SWAT_Hanalei). Data are provided in a compressed folder, SWAT_Hanalei.zip, that contains the input and output files needed to run the model, the calibration data files, and a README_SWAT_Hanalei.txt document that describes the contents of this archive and any additional software needed to execute the model.
DOI: 10.6084/m9.figshare.3761562.v1502
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: 10.6092/unibo/amsacta/6393
title: MADFORWATER. WP4-Field pilots for the adaptation and integration of technologies. Task 4.3-Operation and optimization of the field pilots. Effect of municipal wastewater post-aeration on clogging potential

description: In Agadir, a water-scarce Moroccan region, municipal and industrial wastewater is tertiary-treated to be reused in golf courses. Wastewater reuse has been constrained by severe emitter clogging that caused technical and financial problems. This study aimed to perform an in-depth characterization of the treated wastewater in relation to its susceptibility to cause clogging, and to assess the capacity of an aeration post-treatment to reduce the clogging potential. The post-treatment consisted in injecting different airflows (0-33 L/min/Lreactor) into the treated wastewater. The structural, morphological and elemental composition of the clogging matter collected in the irrigation pipeline was characterized using Scanning Electron Microscopy, Scanning Transmission Electron Microscopy, X-ray Diffraction and X-ray Energy Dispersive Spectroscopy. The 15-day aeration post-treatment at 16.5 L/min/Lreactor presented the best cost-benefit ratio. Organic matter was totally degraded. Calcium was reduced by 9%, bicarbonates by 54%. The analysis of the deposits induced by the aeration post-treatment revealed a relevant decrease of the major constituents of the clogging deposits found in the irrigation pipeline. The results show the effectiveness of post-aeration in biodegrading residual organic matter and precipitating several salts, thus reducing the clogging potential.