cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/5c36d73c524d7
title: Kakadu LIDAR Project 2011 - One Metre Digital Elevation Model Tiled

description: Digital Elevation Model (DEM) 1metre ESRI Grid Float format as 4942 tiles
DOI: 10.25919/5c36d7465fe7b
title: Water table fluctuation recharge estimates for Lower South East of South Australia

description: Recharge estimates Mean for the Lower South East of South Australia using the water table fluctuation method.
DOI: 10.25919/5c36d74f6435e
title: Groundwater bore chemistry samples for flow across faults sites

description: Hydrochemical data for groundwater well samples obtained for the Flow Across Faults component of the Goyder South East project. Data includes measurements of major ion, stable isotope, carbon-14, CFC, SF6  and helium-4 composition.  Data were collected for a range of groundwater wells during November 2012. Well identifiers and locations are included in the data.
DOI: 10.25919/5c36d764b94ef
title: Parkes pulsar observations with undefined project IDs for 2012

description: Parkes pulsar observations with undefined project IDs for 2012
DOI: 10.25919/5c36d76d2ac2f
title: Mean annual water balance estimates for Lower South East of South Australia

description: Mean annual gridded water balance estimates (2001-2010) derived from mean annual AET and mean annual rainfall data for the Lower South East of South Australia. The data is made available as ArcInfo ASCII Grid format.
DOI: 10.25919/5c36d77a6299c
title: CSIRO National Soil Site Database

description: The CSIRO National Soil Site Database (NatSoil) contains descriptions of approximately 16,000 soil site investigations. The data includes morphological descriptions and chemical and mineralogical properties.
DOI: 10.25919/5c36d788c5313
title: Groundwater bore chemistry samples for marine discharge sites

description: Hydrochemical data for groundwater well samples obtained for the Submarine Groundwater Discharge component of the Goyder South East project. Data includes measurements of major ion, stable isotope, radon, radium, and helium-4 composition. Data were collected for a range of groundwater wells during November 2012. Well identifiers and locations are included in the data.
DOI: 10.25911/5c36d7e05b036
title: Upland Heath Swamps Plot Network: Soil Properties, Royal National Park, Sydney Basin, NSW, Australia, 1991-2014
title: Upland Heath Swamps Plot Network: Soil Properties Royal National Park, 1991-2014

description: The Upland Heath Swamps Plot Network Soil Properties data package for Royal National Park contains soil properties data obtained from samples taken within 10 cm of each of the floristics plots (see Upland Heath Swamps Plot Network: Vegetation Floristics, Royal National Park, Sydney Basin, NSW, Australia, 1990+). 

A synopsis of related data packages which have been collected as part of the Upland Heath Swamps Plot Network’s full program is provided at https://doi.org/10.25911/5c130ca59c5a8.
description: AbstractThe Upland Heath Swamps Plot Network Soil Properties data package for Royal National Park contains soil properties data obtained from samples taken within 10 cm of each of the floristics plots (see Upland Heath Swamps Plot Network: Vegetation Floristics, Royal National Park, Sydney Basin, NSW, Australia, 1990+). A synopsis of related data packages which have been collected as part of the Upland Heath Swamps Plot Network’s full program is provided at http://www.ltern.org.au/index.php/ltern-plot-networks/upland-health-swamps.<br><br>**This data package is associated with the following publications:<br><br>[1] Letten, A. D., Keith, D. A., Tozer, M. G., Hui, F. K.C. (2015), Fine-scale hydrological niche differentiation through the lens of multi-species co-occurrence models. Journal of Ecology. doi: 10.1111/1365-2745.12428<br><br>[2] Keith, D. A. and Bradstock, R. A. (1994). Fire and competition in Australian heath: a conceptual model and field investigations. Journal of Vegetation Science 5, 347-354.<br><br>[3] Keith, D. A. (1995a). Mosaics in Sydney heathland vegetation: the roles of fire, competition and soils. CALMScience Supplement 4, 199-206.<br><br>[4] Keith, D. A., Lindenmayer, D. B., Lowe, A.,Russell-Smith, J.,Barrett, S.,Enright N. J., Fox, B. J.,Guerin, G.,Paton, D. C., Tozer, M. G. and Yates, C. J. (2014). Heathlands. In: Biodiversity and Environmental Change: Monitoring, Challenges and Direction. Lindenmayer, D., Burns, E., Thurgate, N., and Lowe, A. Editors, pp215-285. CSIRO, Melbourne.Project fundingSince 2012 this project has been part of the Long Term Ecological Research Network (LTERN). This work was supported by the Australian Government’s Terrestrial Ecosystems Research Network (www.tern.org.au) – an Australian research infrastructure facility established under the National Collaborative Research Infrastructure Strategy and Education Infrastructure Fund–Super Science Initiative through the Department of Industry, Innovation, Science, Research and Tertiary Education.Project nameUpland Heath Swamps Plot Network
DOI: 10.25919/5c36d7c50205a
title: Parkes observations for project P832 semester 2012OCTS

description: PSR J1107-5907 is one of only a few sources known to exhibit an array of emission modulation. From moding in pulse profile shape, to complete cessation and RRAT-like emission, it has everything. It therefore can provide us with useful information about the intrinsic variability of pulsars, as well as help to bridge understanding of observed modulation timescales. It may also retain information that could elucidate the mechanisms responsible for radio emission and its cessation in pulsars. Using Parkes archival data, we have uncovered the extreme variability of PSR J1107-5907. However, the remarkable brightness of the source in one of the modes meant that it saturated the old systems. Moreover, the poor quality of this data meant that we were unable to fully characterise the emission properties of the source. Therefore, we propose new multi-frequency, single-pulse observations of PSR J1107-5907 to investigate these properties in more detail, and accurately characterise the relationship between radio emission moding and cessation in this object. Parkes is the only telescope that can observe this source and the combination of excellent frequency coverage, and polarisation capabilities, make it ideal for these proposed observations. We request 24 hours of observing time.
DOI: 10.25919/5c36d7d22b91c
title: Study area boundary for the Lower South East of South Australia

description: Boundary of the Tertiary confined sand aquifer, which defines the project study area for the Lower South East of South Australia.
DOI: 10.25919/5c36d7e58bb84
title: Kakadu LIDAR Project 2011   0.5m Contours maps

description: 0.5m Contours maps as shape files   4903 tiled files (1km x1km) with DBF, PRJ and SHX support files
DOI: 10.25919/5c36d7ee8730b
title: Parkes observations for project P878 semester 2014OCTS

description: Measuring the magnetic field and electron density of the solar wind is essential in order to understand the properties of solar corona. Detection of dispersion measure (DM) and Faraday rotation measure (RM) of linearly polarized radio sources occulted by the solar wind provides a unique opportunity to measure these properties. Pulsars are therefore ideal sources for such study, especially for millisecond pulsars which can be used to obtain high timing precision to measure the DM variations caused by the solar wind.  Two millisecond pulsars, PSRs J1730-2304 and J1824-2452A with ecliptic latitude of only 0.19 and -1.55 degree are ideal for this work as the line-of-sight to these pulsars goes very close to the Sun; the closest approach is only 0.8 and 5.8 solar radii respectively. We can use these two pulsars detect the different regions of the solar coronal, such as solar equator and south pole region. We also propose a unique study of the solar corona on small physical scales using the lines-of-sight to multiple pulsars in the M28 globular cluster.
DOI: 10.25919/5c36d7f6b2bfc
title: Flight Lines of Flinders River SkyTEM Airborne Electromagnetic Survey

description: Flight lines of a helicopter electromagnetic survey undertaken in the Flinders River catchment of Northern Queensland, Australia. The survey was flown between the 26th November 2012 and 12th December 2012. The dataset contains Electrical Conductivity values (mS/m) at depth intervals between 0 and 225m.
DOI: 10.25919/5c36d800a2026
title: Kakadu LIDAR Project 2011 - LiDAR_Point_Clouds, Classified. AHD

description: LiDAR_Point_Clouds, Classified. AHD have been preocessed to conform to the  Australian Height Datum and converted from files collected as swaths in to tiles of data. The file formats is LAS. 

LAS is an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code     Classification type
0                       Never classified
1                       Unassigned
2                       Ground
3                       Low vegetation
4                       Medium vegetation
5                       High vegetation
6                       Building
7                       Noise
8                       Model key
9                       Water
DOI: 10.25919/5c36d80a465b8
title: Kakadu LIDAR Project 2011 - LiDAR_Point_Clouds, UNClassified. ELL

description: LiDAR_Point_Clouds, UNClassified. ELL Files swaths of flight collected data. Points are located by Elevation, Latitude and Longitude. The file formats is LAS. 

LAS is an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code     Classification type
0                       Never classified
1                       Unassigned
2                       Ground
3                       Low vegetation
4                       Medium vegetation
5                       High vegetation
6                       Building
7                       Noise
8                       Model key
9                       Water
DOI: 10.25919/5c36d812aef9e
title: Parkes observations for project P864 semester 2014APRS

description: The Fast Radio Bursts (FRBs) discovered in 2007 are the very first detections of extragalactic coherent radio emission. Though similar in nature to the giant single pulses emitted by the Rotating Radio Transients (RRATs) in terms of their cold plasma dispersion sweeps and narrow pulse widths, FRBs have not yet been detected in follow up observations at radio or other wavelengths. Our pilot study will examine the feasibility to determine the Rotation Measures (RM) of moderately bright RRAT single pulses similar to FRBs. Since the FRBs are highly energetic pulses of very short duration, they can be assumed to be polarised. This study to measure single pulse RMs could then open up a whole new possibility to map the extragalactic magnetic field of the Universe through FRB polarimetry. At present, we do not know whether cosmological magnetic fields exist, but the detection of sufficiently strong fields would open up a new observational window into the early Universe. The Warm-Hot Intergalactic Medium (WHIM) is believed to be the residing place of the Universeâs missing baryonic matter. If the RM of an FRB can be determined through the diffuse WHIM, it would be the beginning to our understanding of the matter distribution within the cosmic web.
DOI: 10.25919/5c36d81aabe79
title: Kakadu LIDAR Project 2011 Modelkeypoints

description: Model key points are statistically thinned data points that represent the main changes in a sampled surface. The Key Points are classified with code 8 in the LiDAR point classification scheme. Advantages in their use are significant reductions in data volume and reductions in  data noise. Disadvantages are loss small features but potentially significant for certain applications. eg hydrology
DOI: 10.25919/5c36d822f365e
title: Kakadu LIDAR Project 2011   Intensity Mosaic

description: Intensity Mosaic  in ECW format with .ecw.aux.xml, ERS, eww, prj and tab support files
Intensity image as 4943 tiled files (1km x1km) in TIF format with PRJ & TFW support files
DOI: 10.25919/5c36d82fac61c
title: National Livestock Methane Program Project B.CCH.6610 / 01200.038

description: Data was generated for NLMP project entitled "Culture independent metagenomic approaches for understanding the functional metabolic potential of methanogen communities in ruminant livestock"
DOI: 10.25919/5c36d8383c588
title: HyMap surveys acquired during EOC science campaigns

description: HyMap Surveys collected during various science campaigns conducted by the Earth Observation Centre between 1998 and 2003
DOI: 10.25919/5c36d840b6fef
title: Kakadu LIDAR Project 2011 - LiDAR_Point_Clouds, Classified. ELL

description: LiDAR_Point_Clouds, Classified. ELL have been preocessed to from swaths in to tiles of data. Points are located by Elevation, Latitude and Longitude. The file formats is LAS. 

LAS is an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). LAS is a published standard file format for the interchange of lidar data. It maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Each LAS file contains metadata of the lidar survey in a header block followed by individual records for each laser pulse recorded. The header portion of each LAS file holds attribute information on the lidar survey itself: data extents, flight date, flight time, number of point records, number of points by return, any applied data offset, and any applied scale factor. The following lidar point attributes are maintained for each laser pulse of a LAS file: x,y,z location information, GPS time stamp, intensity, return number, number of returns, point classification values, scan angle, additional RGB values, scan direction, edge of flight line, user data, point source ID and waveform information.
Each and every lidar point in a LAS file can have a classification code set for it. Classifying lidar data allows you to organize mass points into specific data classes while still maintaining them as a whole data collection in LAS files. Typically, these classification codes represent the type of object that has reflected the laser pulse. Point classification is usually completed by data vendors using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. The following table contains the LAS classification codes as defined in the LAS 1.1 standard:
Class code     Classification type
0                       Never classified
1                       Unassigned
2                       Ground
3                       Low vegetation
4                       Medium vegetation
5                       High vegetation
6                       Building
7                       Noise
8                       Model key
9                       Water
DOI: 10.25919/5c36d84bae29a
title: Flight Lines of Gilbert River SkyTEM Airborne Electromagnetic Survey

description: Flight lines of a helicopter electromagnetic survey undertaken in the Flinders River catchment of Northern Queensland, Australia. The survey was flown between the 26th November 2012 and 12th December 2012. The dataset contains Electrical Conductivity values (mS/m) at depth intervals between 0 and 225m.
DOI: 10.25919/5c36d85837ee3
title: 9 second substrate rasters for continental modelling of Australia's biodiversity patterns

description: These data provide consistent rasterised layers of edaphic (physical and chemical conditions of the soil) and land surface physiography (landform and geomorphology) variables hypothesised to explain spatial patterns in biological diversity at continental scales for immediate use with statistical modelling tools. These data are intended to be used along with a similarly compiled and spatially standardised set of climatic layers. Consistent "stacks" of raster variables are needed for spatially-explicit biodiversity modelling using tools such as MAXENT or Generalised Dissimilarity Modelling (GDM). Full details of each dataset, with a list of data sources and bibliography, are provided in a table as part of the data collection. Additional information provided with the 1km gridded raster is relevant to some these data and provided here also. Each dataset will need to be separately cited. These data have also been made available for use in the Atlas of Living Australia's Spatial Portal.
DOI: 10.25919/5c36d86a56c0f
title: Dispersive diffusion of gases in coals

description: Dispersive diffusion of gases in coals Part 1 raw data from experiments
DOI: 10.25919/5c36d874437d9
title: The 23 year PSR B1259-63 dataset

description: Pulsar time of arrival (TOA) observations of PSR B1259-63, a 48 ms spin-period pulsar with an main-sequence Oe companion, LS 2883.   The collection comprises (TOAs)   taken with the Parkes telescope the  from early 1990 to early 2013, and a best-fit model.   The model incorporates changes in the binary orbit due to interactions between the pulsar and its companion, a glitch in the pulsars spin-down, and significant red spin noise.
DOI: 10.25919/5c36d87de0125
title: Kakadu LIDAR Project 2011 - One Metre Digital Surface Model (DSM)Tiled

description: Digital Surface Model (DSM) 1metre ESRI Grid Float format as 4942 tiles.

There is no common usage of the terms digital elevation model (DEM), digital terrain model (DTM) and digital surface model (DSM) in scientific literature. In most cases the term digital surface model represents the earth's surface and includes all objects on it. In contrast to a DSM, the digital terrain model (DTM) represents the bare ground surface without any objects like plants and buildings. Digital Elevation Model (DEM) and Digital Terrain Model (DTM)  appear to be used interchangeably.
DOI: 10.25919/5c36d886bab1b
title: Parkes observations for project P455 semester 2013OCTS

description: This project is to continue timing and profile studies of the first double-pulsar system, a unique laboratory for gravitational physics. The main aims of this proposal are to provide the strongest tests to date for general relativity and to measure for the first time the moment-of-inertia of a neutron star. Additionally, we will determine the system geometry and map the pulsar beams via geodetic precession.
DOI: 10.25919/5c36d89932ad0
title: Parkes observations for project P602 semester 2013OCTS

description: We propose primarily to continue studying 1E 1547.0-5408, one of the
three magnetars known to currently emit radio pulsations. The aim is to
develop a more specific understanding of the dynamical behavior of their
magnetospheres and ultra-strong magnetic fields, which are responsible
for X-ray and gamma-ray outbursts, magnetospheric currents and particle
acceleration, and heating of the neutron star surface. 1E 1547.0-5408
has been remarkably active over the past 4.5 years: following a large
X-ray outburst in October 2008, radio emission ceased, recommencing
sporadically in December. In January 2009, a much larger X-ray outburst
occurred, and since then there have been huge variations in its radio
flux.  The X-ray flux is gradually decaying, and meanwhile the torque
on the star continues to vary enormously. At Parkes we request weekly
timing to track its rotation and radio flux, while we monitor its X-ray
flux with the Swift satellite. For little additional time we will also
obtain useful data on two other pulsars of interest: one other
magnetar known to be currently radio active, and a pulsar that "blinks"
on ~year timescales, the second known in its class.