cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5079762
title: Data used in "The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations"

description: These are the data used in the analysis and creation of figures in du Plessis et al. 2022: <em>The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations</em> in support of open-code, transparency, and repeatability. Abstract: Water mass transformation in the Southern Ocean is vital for driving the large-scale overturning circulation, which transports heat from the surface to the ocean interior. Using profiling gliders, this study investigates the role of summertime buoyancy forcing and wind-driven processes on the intraseasonal (1-10 days) mixed layer thermohaline variability in three Southern Ocean regions southwest of Africa important for water mass transformation - the Subantarctic Zone (SAZ), Polar Frontal Zone (PFZ) and Marginal Ice Zone (MIZ). At intraseasonal time scales, heat flux was shown as the main driver of buoyancy gain in all regions. In the SAZ and MIZ, shallow mixed layers and strong stratification enhanced mixed layer buoyancy gain by trapping incoming heat, while buoyancy loss resulted primarily from the entrainment of cold, salty water from below. In the PFZ, rapid mixing linked to Southern Ocean storms set persistently deep mixed layers and suppressed mixed layer intraseasonal (1-10 days) thermohaline variability. In the polar regions, lateral stirring of meltwater from seasonal sea-ice melt dominated daily mixed layer salinity variability. We propose that these meltwater fronts are advected to the PFZ during late summer, indicating the potential for seasonal sea-ice freshwater to impact a region where the upwelling limb of overturning circulation reaches the surface. This study reveals a regional dependence of how the mixed layer thermohaline properties respond to small spatio-temporal processes, emphasizing the importance of surface forcing occurring between 1-10 days on the mixed layer water mass transformation in the Southern Ocean. <strong>Related code:</strong> The accompanying code can be found at: http://doi.org/10.5281/zenodo.5076119. Navigate to github.com/marcelduplessis/duplessis-2021-SO-thermohaline for the latest version
DOI: 10.5281/zenodo.5079763
title: Data used in "The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations"

description: These are the data used in the analysis and creation of figures in du Plessis et al. 2022: <em>The daily-resolved Southern Ocean mixed layer: regional contrasts assessed using glider observations</em> in support of open-code, transparency, and repeatability. Abstract: Water mass transformation in the Southern Ocean is vital for driving the large-scale overturning circulation, which transports heat from the surface to the ocean interior. Using profiling gliders, this study investigates the role of summertime buoyancy forcing and wind-driven processes on the intraseasonal (1-10 days) mixed layer thermohaline variability in three Southern Ocean regions southwest of Africa important for water mass transformation - the Subantarctic Zone (SAZ), Polar Frontal Zone (PFZ) and Marginal Ice Zone (MIZ). At intraseasonal time scales, heat flux was shown as the main driver of buoyancy gain in all regions. In the SAZ and MIZ, shallow mixed layers and strong stratification enhanced mixed layer buoyancy gain by trapping incoming heat, while buoyancy loss resulted primarily from the entrainment of cold, salty water from below. In the PFZ, rapid mixing linked to Southern Ocean storms set persistently deep mixed layers and suppressed mixed layer intraseasonal (1-10 days) thermohaline variability. In the polar regions, lateral stirring of meltwater from seasonal sea-ice melt dominated daily mixed layer salinity variability. We propose that these meltwater fronts are advected to the PFZ during late summer, indicating the potential for seasonal sea-ice freshwater to impact a region where the upwelling limb of overturning circulation reaches the surface. This study reveals a regional dependence of how the mixed layer thermohaline properties respond to small spatio-temporal processes, emphasizing the importance of surface forcing occurring between 1-10 days on the mixed layer water mass transformation in the Southern Ocean. <strong>Related code:</strong> The accompanying code can be found at: http://doi.org/10.5281/zenodo.5076119. Navigate to github.com/marcelduplessis/duplessis-2021-SO-thermohaline for the latest version
DOI: 10.6084/m9.figshare.21878604.v1
title: Supplementary Material for: <b><i>CYP1B1</i></b> Genetic Variants (rs10916 and rs2855658) Are Associated with Increased Risk of Ischemic Stroke in Chinese Han Population

description: <b><i>Introduction:</i></b> Ischemic stroke (IS) is an extremely complex disease caused by the combined action of multiple environmental and genetic factors. <i>CYP1B1</i> is a member of the cytochrome P450 protein family, and it is an important human drug-metabolizing enzymes. We aimed to explore the association between <i>CYP1B1</i> genetic variants and IS risk in Chinese Han population. <b><i>Methods:</i></b> We recruited 1,150 participants to conduct a “case-control” study. The assessment of association between candidate <i>CYP1B1</i> genetic variants (rs2855658, rs10916, rs162560, rs2567206) and IS risk was performed by SNPStats online software. In addition, false-positive report probability analysis was used to detect whether the positive findings were just chance or noteworthy observations. Finally, the interaction of candidate SNPs in IS risk was evaluated by multifactor dimensionality reduction. <b><i>Results:</i></b> The results showed that <i>CYP1B1</i>-rs2855658 was a risk factor for IS among ≥60-year-old (dominant: <i>p</i> = 0.034; overdominant: <i>p</i> = 0.026), smoking (heterozygote: <i>p</i> = 0.009; dominant: <i>p</i> = 0.004; overdominant: <i>p</i> = 0.012; log-additive: <i>p</i> = 0.003), and drinking participants (homozygous: <i>p</i> = 0.036; dominant: <i>p</i> = 0.019; recessive: <i>p</i> = 0.012; log-additive: <i>p</i> = 0.006). <i>CYP1B1</i>-rs10916 also was a risk factor for IS patients among ≥60-year-old (heterozygote: <i>p</i> = 0.047; overdominant: <i>p</i> = 0.048), smoking (dominant: <i>p</i> = 0.050; overdominant: <i>p</i> = 0.049), and drinking participants (dominant: <i>p</i> = 0.019; overdominant: <i>p</i> = 0.038; log-additive: <i>p</i> = 0.013). <b><i>Conclusion:</i></b> <i>CYP1B1</i>-rs10916 and <i>CYP1B1</i>-rs2855658 can increase the IS risk in Chinese Han population who are ≥60 years old, smoking, or drinking alcohol.
DOI: 10.5281/zenodo.7528638
title: XES Logistics And Transportation Dataset - Large (~19 Days)

description: <strong>Vienna Tram Line 71 (days 2022-12-06 to 2022-12-07): Delays, Weather, Trafic, Construction Sites</strong> This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through https://cpee.org. The data set contains raw data and refined and aggregated data in the XES SensorStream format https://arxiv.org/abs/2206.11392. The dataset contains data from the following sources: Tram line delay data & construction sites in the vicinity of the tram line stops: https://digitales.wien.gv.at/open-data/. All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis). Traffic data in the vicinity of the tram line stops: https://developer.tomtom.com/store/maps-api/. We use data gathered through the TomTom "Freemium" plan, which is covered by https://developer.tomtom.com/terms-and-conditions/. To the best of our knowledge the gathered traffic flow data can be freely distributed and used for analysis purposes, as TomTom invites researchers (and business) to explore new applications. Weather data: https://openweathermap.org/. We collect data based on the "Free" plan, which is covered by a CC BY-SA 4.0 license (https://openweathermap.org/full-price#licenses).
DOI: 10.5281/zenodo.7528637
title: XES Logistics And Transportation Dataset - Large (~19 Days)

description: <strong>Vienna Tram Line 71 (days 2022-12-06 to 2022-12-07): Delays, Weather, Trafic, Construction Sites</strong> This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through https://cpee.org. The data set contains raw data and refined and aggregated data in the XES SensorStream format https://arxiv.org/abs/2206.11392. The dataset contains data from the following sources: Tram line delay data & construction sites in the vicinity of the tram line stops: https://digitales.wien.gv.at/open-data/. All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis). Traffic data in the vicinity of the tram line stops: https://developer.tomtom.com/store/maps-api/. We use data gathered through the TomTom "Freemium" plan, which is covered by https://developer.tomtom.com/terms-and-conditions/. To the best of our knowledge the gathered traffic flow data can be freely distributed and used for analysis purposes, as TomTom invites researchers (and business) to explore new applications. Weather data: https://openweathermap.org/. We collect data based on the "Free" plan, which is covered by a CC BY-SA 4.0 license (https://openweathermap.org/full-price#licenses).
DOI: 10.20372/eiar-rdm/esm55e/5pipqb
title: 01 Environment and genotypes final manuscript coppy Page 1-27.pdf

description: :unav
DOI: 10.5281/zenodo.2783955
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API  (via Tweepy) using the following filters:


	terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em>
	language: <em>'de'</em>


This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>).

This dataset might be useful for the following use cases:


	Natural language processing (focussing on Twitter specifics in German, there exist only little datasets focussing this specific aspect)
	Social Network Analysis (Twitter network)
	Identifying behavioral patterns (retweeting, quoting, replying, hate speech, ...)
	Sharing political (or other domain-specific) content
	Bot detection
	and more ...


This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern:


	german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB)


It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 25k recorded tweets/accounts (size: ~ 3MB).
DOI: 10.5281/zenodo.3236750
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API  (via Tweepy) using the following filters:


	terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em>
	language: <em>'de'</em>


This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>).

This dataset might be useful for the following use cases:


	Natural language processing (focussing on Twitter specifics in German, there exist only little datasets focussing this specific aspect)
	Social Network Analysis (Twitter network)
	Identifying behavioral patterns (retweeting, quoting, replying, hate speech, ...)
	Sharing political (or other domain-specific) content
	Bot detection
	and more ...


This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern:


	german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB)


It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 50k recorded tweets/accounts (size: ~ 6MB).
description: The german-tweet-sample-2019-05 sub-dataset contains the  2019 elections for the European Parliament #EP2019.
DOI: 10.5281/zenodo.3265642
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API  (via Tweepy) using the following filters:


	terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em>
	language: <em>'de'</em>


This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>).

This dataset might be useful for the following use cases:


	Natural language processing (focussing on Twitter specifics in German, there exist only little datasets focussing this specific aspect)
	Social Network Analysis (Twitter network)
	Identifying behavioral patterns (retweeting, quoting, replying, hate speech, ...)
	Sharing political (or other domain-specific) content
	Bot detection
	and more ...


This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern:


	german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB)


It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 50k recorded tweets/accounts (size: ~ 6MB).
description: The german-tweet-sample-2019-05 sub-dataset contains the  2019 elections for the European Parliament #EP2019.
DOI: 10.5281/zenodo.3633935
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 50k recorded tweets/accounts (size: ~ 6MB).
DOI: 10.5281/zenodo.3693277
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
DOI: 10.5281/zenodo.3736276
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic
DOI: 10.5281/zenodo.3783478
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic
DOI: 10.5281/zenodo.3877979
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic
DOI: 10.5281/zenodo.3925315
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic
DOI: 10.5281/zenodo.3969850
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic
DOI: 10.5281/zenodo.4011608
title: Monthly Samples of German Tweets

description: This dataset contains German tweets and Twitter accounts recorded from the public Twitter Streaming API using the following filters: terms: <em>'a'</em>, <em>'e'</em>, <em>'i'</em>, <em>'o'</em>, <em>'u'</em>, and <em>'n'</em> language: <em>'de'</em> This filter combination should record (almost) all German tweets (in German it is very unlikely that terms do not contain vowels or the frequently used character <em>'n'</em>). This dataset might be useful for the following use cases: Natural language processing (focussing on Twitter specifics in German, there exist only little German datasets) Social Network Analysis (Twitter network) Identifying behavioural patterns (retweeting, quoting, replying, hate speech, ...) Sharing political (or other domain-specific) content Bot detection and more ... This dataset will be updated monthly. Each sample (starting in April 2019) will follow the following naming pattern: german-tweet-sample-<em>&lt;YEAR&gt;</em>-<em>&lt;MONTH&gt;</em>.zip (size: ~ 1GB) It will contain several bunches of recorded JSON gzipped files. Each bunch of records contains approximately 150k recorded tweets/accounts (size: ~ 18MB).
description: contains Corona Pandemic