cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh2833747.08fu
title: SH2833747.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2833748.08fu
title: SH2833748.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1856683.08fu
title: SH1856683.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1856684.08fu
title: SH1856684.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2833749.08fu
title: SH2833749.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2833750.08fu
title: SH2833750.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1856685.08fu
title: SH1856685.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2833751.08fu
title: SH2833751.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.25919/c26b-3t56
title: Parkes observations for project P1145 semester 2022APRS_04

description: Pulsar surveys find pulsars, but follow-up observations are crucial to establish the nature of the pulsars, including their spin-down rate, their magnetic field strength, their polarization properties and whether they are part of a binary system and therefore potentially interesting for further study. We aim to determine timing solutions for 26 pulsars discovered at Parkes but which have not been observed since their discovery.
DOI: 10.25919/vwwb-e373
title: Parkes observations for project P1054 semester 2022APRS_12

description: With this proposal we ask time to continue our timing follow-up campaign of pulsars discovered with the MeerKAT telescope. Fourteen sources have been discovered in targeted observations of Fermi unidentified point sources, three in the Small Magellanic Cloud, 37 in Globular Clusters and 34 in a survey of the Galactic plane. A large fraction of the new discoveries are recycled pulsars (including at least one double neutron star system and several 'spider' binaries), or young pulsars. Timing observations have an essential role in exploiting the full potential of any pulsar discovery, allowing the precise measurement of rotational, astrometric and orbital parameters which, in turn, give us powerful tools to improve our understanding of the physics in extreme environments as well as of the population of neutron stars as a whole. The UWL receiver of the Parkes telescope is a sensitive, versatile instrument that is allowing us to successfully time these new sources, in the bright-end of TRAPUM discoveries. The UWL has also the unique advantage of allowing detailed studies of the eclipses of the so-called 'spider' pulsars, which make up about 1/3rd of the sources we aim at following-up with this project.
DOI: 10.5281/zenodo.6778579
title: Dataset of Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy

description: The dataset includes search results used in article “Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy” biorXiv, 2022.01.17.476562, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2022.01.17.476562 Search Results Description: Please use Figure 1 from paper to trace the data made available and experimental group. The excel “raw data 2022-06-24” file contains the following data Body weight of each rabbit on a weekly basis Analysis of breeding parameters at mid-pregnancy Histological areas of each mammry tissue measured Optical density values obtained for biochemical leptin concentration determination Optical density values obtained for biochemical triglyceride concentration determination Optical density values obtained for biochemical glucose concentration determination Optical density values obtained for biochemical cholesterol concentration determination RT-qPCR results (Ct) from QuantStudio export for milk protein analysis RT-qPCR results (Ct) from QuantStudio export for lipid metabolism analysisen “Statistical analysis.doc” contained the description of the statistics used in Excel and the description of the linear mixed model analysis , the reference of the script available by the CRAN project is also include.The R scipt file for using the linear mixed model in R added .
DOI: 10.25919/yf2k-my72
title: Regulated and unregulated stream reaches in the Murray Darling Basin within the network of the AWRA-R river systems model

description: This dataset contains a classification of the stream reaches in the Murray Darling Basin as either regulated or unregulated within the network of the AWRA-R river systems model. The dataset was created for the purposes of identifying which reaches are potentially impacted by river regulation and could potentially be capable of receiving environmental water releases from storages. The definition of a regulated reach that has been adopted here is simply being downstream of a major storage. This is a much more inclusive definition of regulated than is adopted for water resources applications: “River on which a licensed entitlement regime exists with centralised allocation, and from which orders may be placed for upstream release of a licensed allocation” (http://www.bom.gov.au/water/awid/id-996.shtml). The dataset contains a figure displaying the data (reg_unreg.png), the major storages dataset (MDB_reservoirs_250K.shp) and a shapefile for the stream reaches (AWRA-Rv5_MDB_streams_reg_unreg.shp). The stream reaches shapefile has Reach ID and ID_updated fields as used in AWRA-R, there are also fields for GaugeID and GaugeName which are the downstream gauge number and name and a regulation field that contains the classification as either Regulated or Unregulated.
DOI: 10.6084/m9.figshare.19539004
title: Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among (peri)urban wildlife populations

description: In this study, we combined social network analysis with epidemiological Susceptible Infected Recovered (SIR) models, to investigate the effects of macaques' (<em>Macaca</em> spp) co-occurrence and joint interactions with humans, and their social grooming of conspecifics, on simulated disease outbreaks. On each of 10 macaque groups representing three different species living in anthropogenic (peri)urban environments in India and Malaysia, we collected behavioral data on macaques' tendencies to co-occur and jointly engage in interactions (e.g. aggression, provisioning, snatching food) with humans within the same time and space, and their social grooming (cleaning and manipulating the fur of another individual) of their conspecifics (see our previous publications: Balasubramaniam et al., 2020, 2021). The data were used to construct both 'human co-interaction networks' and 'social grooming networks'. For each group and network type, we then ran 5000 SIR model simulations. These were run for pathogens of varying transmissibility, by randomly selecting a macaque to be infected first. The model simulation then proceeds until the pathogen reaches saturation or extinction. It does so by allowing animals to move through animals moving across disease states based on dynamic probabilities that are functions of both their network connectedness and pathogen transmissibility. <br> The dataset uploaded is the outcome of these simulations. For each "first infected macaque", we have included the average outbreak size from across the simulations in which it was the first infected individual. This information has been paired with various sociodemographic (e.g. group size, macaque species, sex of the individual) and behavioral characteristics (e.g. their direct and indirect connectedness or centrality within their human co-interaction networks and grooming networks, dominance ranks, and anthropogenic interactions indicated by their frequencies of interactions with humans and proportions of time spent foraging on human foods). The dataset was used to run GLMMs aimed at examining the effects of the above sociodemographic attributes and behavioral traits of first-infected individuals on the outcome of model-predicted disease outbreak sizes. Thus, we have also provided a second file that contains R code for the GLMMs. <br> The datasets provided were used to write a manuscript that is currently under review in the journal <em>Scientific Reports. </em>They have not been published elsewhere, or made available elsewhere.
DOI: 10.6084/m9.figshare.19539004.v3
title: Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among (peri)urban wildlife populations

description: In this study, we combined social network analysis with epidemiological Susceptible Infected Recovered (SIR) models, to investigate the effects of macaques' (<em>Macaca</em> spp) co-occurrence and joint interactions with humans, and their social grooming of conspecifics, on simulated disease outbreaks. On each of 10 macaque groups representing three different species living in anthropogenic (peri)urban environments in India and Malaysia, we collected behavioral data on macaques' tendencies to co-occur and jointly engage in interactions (e.g. aggression, provisioning, snatching food) with humans within the same time and space, and their social grooming (cleaning and manipulating the fur of another individual) of their conspecifics (see our previous publications: Balasubramaniam et al., 2020, 2021). The data were used to construct both 'human co-interaction networks' and 'social grooming networks'. For each group and network type, we then ran 5000 SIR model simulations. These were run for pathogens of varying transmissibility, by randomly selecting a macaque to be infected first. The model simulation then proceeds until the pathogen reaches saturation or extinction. It does so by allowing animals to move through animals moving across disease states based on dynamic probabilities that are functions of both their network connectedness and pathogen transmissibility. <br> The dataset uploaded is the outcome of these simulations. For each "first infected macaque", we have included the average outbreak size from across the simulations in which it was the first infected individual. This information has been paired with various sociodemographic (e.g. group size, macaque species, sex of the individual) and behavioral characteristics (e.g. their direct and indirect connectedness or centrality within their human co-interaction networks and grooming networks, dominance ranks, and anthropogenic interactions indicated by their frequencies of interactions with humans and proportions of time spent foraging on human foods). The dataset was used to run GLMMs aimed at examining the effects of the above sociodemographic attributes and behavioral traits of first-infected individuals on the outcome of model-predicted disease outbreak sizes. Thus, we have also provided a second file that contains R code for the GLMMs. <br> The datasets provided were used to write a manuscript that is currently under review in the journal <em>Scientific Reports. </em>They have not been published elsewhere, or made available elsewhere.
DOI: 10.6084/m9.figshare.19539004.v2
title: Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among (peri)urban wildlife populations

description: In this study, we combined social network analysis with epidemiological Susceptible Infected Recovered (SIR) models, to investigate the effects of macaques' (<em>Macaca</em> spp) co-occurrence and joint interactions with humans, and their social grooming of conspecifics, on simulated disease outbreaks. On each of 10 macaque groups representing three different species living in anthropogenic (peri)urban environments in India and Malaysia, we collected behavioral data on macaques' tendencies to co-occur and jointly engage in interactions (e.g. aggression, provisioning, snatching food) with humans within the same time and space, and their social grooming (cleaning and manipulating the fur of another individual) of their conspecifics (see our previous publications: Balasubramaniam et al., 2020, 2021). The data were used to construct both 'human co-interaction networks' and 'social grooming networks'. For each group and network type, we then ran 5000 SIR model simulations. These were run for pathogens of varying transmissibility, by randomly selecting a macaque to be infected first. The model simulation then proceeds until the pathogen reaches saturation or extinction. It does so by allowing animals to move through animals moving across disease states based on dynamic probabilities that are functions of both their network connectedness and pathogen transmissibility. <br> The dataset uploaded is the outcome of these simulations. For each "first infected macaque", we have included the average outbreak size from across the simulations in which it was the first infected individual. This information has been paired with various sociodemographic (e.g. group size, macaque species, sex of the individual) and behavioral characteristics (e.g. their direct and indirect connectedness or centrality within their human co-interaction networks and grooming networks, dominance ranks, and anthropogenic interactions indicated by their frequencies of interactions with humans and proportions of time spent foraging on human foods). The dataset was used to run GLMMs aimed at examining the effects of the above sociodemographic attributes and behavioral traits of first-infected individuals on the outcome of model-predicted disease outbreak sizes. Thus, we have also provided a second file that contains R code for the GLMMs. <br> The datasets provided were used to write a manuscript that is currently under review in the journal <em>Scientific Reports. </em>They have not been published elsewhere, or made available elsewhere.
DOI: 10.25919/xz5x-h444
title: East Australian Current mooring raw data collection 2015-2016

description: The East Australian Current mooring raw data collection 2015-2016 contains all the raw instrument data files and instrument setup information, build documentation, deployment and recovery log sheets and scanned paper documentation for the deployment covering the period May, 2015 to November, 2016. Quality controlled data and gridded data products from this mooring deployment can be accessed via the links in the Related Links section of this collection record.
DOI: 10.25919/4dnf-2e07
title: East Australian Current mooring raw data collection 2016-2018

description: The East Australian Current mooring raw data collection 2016-2018 contains all the raw instrument data files and instrument setup information, build documentation, deployment and recovery log sheets and scanned paper documentation for the deployment covering the period November, 2016 to May, 2018. Quality controlled data and gridded data products from this mooring deployment can be accessed via the links in the Related Links section of this collection record.
DOI: 10.25919/d2rj-rd57
title: East Australian Current mooring raw data collection 2012-2013

description: The East Australian Current mooring raw data collection 2012-2013 contains all the raw instrument data files and instrument setup information, build documentation, deployment and recovery log sheets and scanned paper documentation for the deployment covering the period April, 2012 to August, 2013. Quality controlled data and gridded data products from this mooring deployment can be accessed via the links in the Related Links section of this collection record.
DOI: 10.7910/dvn/a1wasi
title: Test data

description: Test