cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/0vm7-6b16
title: Parkes observations for project P1068 semester 2020APRS_27

description: In the fast-moving world of Fast Radio Bursts (FRBs), the key question of what fraction (as high as 100%?) of FRBs emit repeat bursts remains unanswered. Of order 10 repeating FRBs are now known, and their spectro-temporal properties hint at differences with apparently non-repeating bursts - but poor statistics preclude a definitive diagnosis. FRB 180301 is a promising source for further study for multiple reasons: it is the first repeating FRB seen by the FAST telescope, and has the second-highest rotation measure ever seen for a FRB (after the original repeater, FRB121102). We request Parkes observations with the UWL to characterise the broadband emission properties of this FRB, with the following key goals: to constrain the repetition rate, identify any variation of pulse characteristics over time, and provide insight into the emission mechanisms. In its initial detection, FRB 180301 was poorly fit by regular Faraday rotation models, and showed significant circular polarization, which may indicate the progenitor is embedded in an extreme magneto-ionic environment with complex field structure that has not been seen before. These observations will generate a large data volume. We will use the Medusa backend in parallel with the Breakthrough Listen data recorder (BL), and will transfer data to BL immediately after observations.These data will be used by P. Kumar to prototype a FRB pipeline for the UWL, that will be made broadly available for similar studies.
DOI: 10.5281/zenodo.4274930
title: SLURP: A Spoken Language Understanding Resource Package

description: This resource contains supplementary audio material used in our EMNLP 2020 paper where we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. Textual annotations are released via the corresponding Github repository (for ease of maintenance).
DOI: 10.5281/zenodo.4274929
title: SLURP: A Spoken Language Understanding Resource Package

description: This resource contains supplementary audio material used in our EMNLP 2020 paper where we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. Textual annotations are released via the corresponding Github repository (for ease of maintenance).
DOI: 10.21227/7nd2-y655
title: AI_QSample_012

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/pkx0-e983
title: AI_QSample_015

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/j544-6d39
title: AI_QSample_017

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/qve7-mv87
title: AI_QSample_018

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.25919/k0ze-ya06
title: Increased virulence of Puccinia coronata f. sp. avenae populations through allele frequency changes at multiple putative Avr loci.

description: These are VCF files that represent genomic variation of 60 Puccinia coronata f. sp. avenae isolates against two genome references. Raw Illumina sequencing reads used in this study are available in the NCBI BioProject PRJNA398546.
DOI: 10.21227/0t5z-6122
title: RF

description: RF model
DOI: 10.21227/tm5c-gj25
title: AI_QSample_019

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.21227/9w7b-8q26
title: AI_QSample_020

description: During the occupancy of a given tenant, the electricity usage of a house is subject to unexpected fluctuation. One notable example is when the house tenets were outstation for a few days (eg on vacation). in such a case, the trend of electric usage is characterized by a very abrupt drop of electricity, which is then followed by a period of very low variations. On the other hand, a house may experience a sudden increase of electrical usage due to special events or guests, or when the house in undergoing on-site renovations or construction projects.Sometimes, a house may be left vacant as the owner moved to a different house. Moreover, change of tenants may also occur. And even among those houses where the same tenants remains, they may from time to time renovate their houses or even hire building contractors to do so. Such act will permanently change the electric consumption. In such a case, only the most recent cases will be considered.In determining the estimated kWh spent of a month, an electrician will disregard all the incidental cases and will only consider the usual trend, ie when the tenants are carrying on their normal daily lifestyles. The electrical consumption fo much daily lifestyle will hence mostly depends on the temperature. In general, the electric consumption is higher in the colder months of the year. The changing lifestyle of the tenants, though to a lesser extent, also contribute to the permanent change of the electric usage.Our program first work on determining the useful portion of each data by comparing the trends of change across the entire 365 days. First and foremost, our program considers every single number of data as represented. And we have developed out own novel way of similarity measures chich takes even the time of a day into accounts. Such definition of similarity measures enables us to quantitatively determine the differences of consumption pattern across the entire year, yet taking every half hourly data into account. Then, our program will detect all abrupt changes from the pattern it observed, the program will determine on its own, whether such abrupt changes are temporary or permanent. Our program is even able to determine whether such changes are due to even or vacant, and will assign the minimum number of days to be considered a permanent change, based on the nature of such abrupt change. Even for the slight changes of electricity consumption due to gradual change of lifestyle, our program can still detect it via quantitatively measuring the symmetry of those graphs.
DOI: 10.6084/m9.figshare.13239512
title: For fgsea docker image

description: This is test data to be used for fgsea docker image at https://hub.docker.com/u/asidd13
DOI: 10.6084/m9.figshare.13239512.v1
title: For fgsea docker image

description: This is test data to be used for fgsea docker image at https://hub.docker.com/u/asidd13
DOI: 10.21227/h6ce-cm73
title: Flooding of broadcast messages in FANET with fixed topologies

description: These are OMNeT++ flooding simulation results of four-UAV FANET in three different topologies, as mentioned in "The Broadcast Storm Problem in FANETs and the Dynamic Neighborhood-Based Algorithm as a Countermeasure".Please, check the paper for more information.
DOI: 10.3886/e126561
title: Using Mixture Modelling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

description: Objectives: Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modelling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning inter-individual variability and intraindividual change in patterns of cognitive aging. <br><br>Method: We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period. <br><br>Results: There were four subgroups at each point in time. Approximately three-quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory. <br><br>Discussion: Results demonstrate the utility of using mixture modelling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.
DOI: 10.3886/e126561v1
title: Using Mixture Modelling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

description: Objectives: Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modelling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning inter-individual variability and intraindividual change in patterns of cognitive aging. <br><br>Method: We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period. <br><br>Results: There were four subgroups at each point in time. Approximately three-quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory. <br><br>Discussion: Results demonstrate the utility of using mixture modelling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.
DOI: 10.21227/em3p-sf86
title: Flooding of broadcast messages in FANET with fixed topologies

description: These are OMNeT++ flooding simulation results of four-UAV FANET in three different topologies, as mentioned in "The Broadcast Storm Problem in FANETs and the Dynamic Neighborhood-Based Algorithm as a Countermeasure".Please, check the paper for more information.
DOI: 10.6084/m9.figshare.13238816
title: Figure6 data

description: Surface air temperature and surface O3 concentrations from observations and control simulation.
DOI: 10.6084/m9.figshare.13238816.v1
title: Figure6 data

description: Surface air temperature and surface O3 concentrations from observations and control simulation.
DOI: 10.6084/m9.figshare.13238822
title: Figure7 data

description: The vertical profiles of cloud top cooling rate, turbulent diffusion coefficient and potential temperature from observation and sensitivity experiments. The surface O3 from observation and sensitivity experiments.