cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/5c36d8a3b62f3
title: Parkes observations for project P789 semester 2014APRS

description: Recently there has been great success at Parkes discovering millisecond pulsars (MSPs), a class that includes some of the most interesting and scientifically valuable pulsars known. In the last few years we have discovered more than 40 MSPs though the High Time Resolution Universe Survey (P630) and Fermi searches (P675/P814). Here we propose to carry out precision timing of these sources to fully exploit these discoveries. Additionally, continue to study the most significant of the discoveries from the Parkes Multibeam Pulsar Survey (P268). 
To maximise scientific impact we have combined the most important sources from various previous proposals into this project, reducing total observation time required and easing the burden on the scheduler.
This project also makes use of all beams of the 21-cm receiver to search for transient radio burst of extragalactic origin at the same time as performing timing observations.
DOI: 10.25919/5c36d8b09a4a4
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: 10.25919/5c36d8be43089
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: 10.25919/5c36d8cc037a6
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: 10.25919/5c36d8de000f6
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: 10.25919/5c36d8e6d69e8
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: 10.25919/5c36d8f13f6f4
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: 10.25919/5c36d9006cfbe
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: 10.25919/5c36d91103f49
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: 10.25919/5c36d91a050a3
title: Kakadu LIDAR Project 2011,  Two metre Canopy Height Model (CHM) Tiled,

description: Canopy Height Model (CHM)
  2 metre ESRI Grid Float format as 4944 tiles
DOI: 10.25919/5c36d92307738
title: Kakadu LIDAR Project 2011, Ten Metre  Forest Canopy Model (FCM) Tiled

description: Forest Canopy Model (FCM)
       10metre ESRI Grid Float format as 4944 tiles
DOI: 10.25919/5c36d93289ef2
title: Parkes observations for project P858 semester 2014APRS

description: SUPERB is a large-scale survey for pulsars and extragalactic radio bursts. It will use highly optimised GPU codes to search for pulsars and fast radio bursts (FRBs), making discoveries in real time. Handling our data as it comes in is essential for the SKA Phase I era so this work applies directly to the high-data rates of next generation telescopes. The pulsars discovered will enable studies of the interstellar medium, allow us to more accurately constrain the MSP luminosity function (which informs estimates of the SKA yield of MSPs), tests of theories of gravity and several will contribute to the precision timing projects of the PPTA. The FRBs discovered will have much more associated information than all previous detections. Firstly the discovery lag will be ~1 second, rather than months/years. The Parkes observations will be shadowed by the Molonglo telescope to allow, for the first time, localisation of FRBs, and a host of optical and high-energy telescopes will then be triggered as appropriate. This is key for identifying FRB host galaxies, so as to solve the mystery of their progenitors. The survey will discover ~20 MSPs, ~100 slower pulsars and ~10 FRBs.
DOI: 10.25919/5c36d93e8b28f
title: Parkes observations for project P866 semester 2014OCTS

description: Studies of the 23 MSPs in the globular cluster (GC) 47 Tucanae (47 Tuc) have provided a wealth of interesting science. Timing of these MSPs (all with periods between 2 and 8 ms) has enabled us to probe the environment and dynamics of the cluster in impressive detail and has allowed us to put constraints on the masses of several of the pulsars. However, previous studies of the cluster were limited by the hardware used. The AFB provided 1-bit digitisation and relatively coarse 88 us sampling. Through the use of the CASPSR backend, we will observe 47 Tuc and its 23 MSPs with unprecedented time resolution. For timing we will achieve almost two orders of magnitude better time resolution, allowing us to resolve out features in the profiles of several cluster pulsars, greatly improving their timing accuracy. For search purposes the CASPSR backend will provide a coherently dedispersed filterbank file at the cluster's dispersion measure. We will search this data using state-of-the-art GPU acceleration searching codes to hunt for compact binary systems.  In tandem with timing and searching, we will perform real-time transient searching to hunt for fast radio bursts (FRBs). Through shadowing with the Molonglo telescope, we will be able to provide the best localisation, spectral index measurement and repetition limits for any detected FRB. This will provide us with much needed information for multi-wavelength follow-up and will give new insight into the origins of these mysterious events.
DOI: 10.25919/5c36d94e17583
title: Parkes observations for project P869 semester 2014APRS

description: Pulsars found closely orbiting the supermassive black hole candidate at the centre of our Galaxy, Sgr A*, could be used to perform the most precise tests of theories of gravity, and to accurately measure the properties of the black hole itself, such as the mass and spin. These measurements can be used to test the no hair theorem and cosmic censorship conjecture in Einsteins theory of General Relativity. The recent discovery of a magnetar in the immediate vicinity of Sgr A* prooves pulsars are indeed present in this region and detectable at lower frequencies than previously thought. We propose to perfom deep searches for such pulsars.
DOI: 10.25919/5c36d95a24d4c
title: Parkes observations for project P873 semester 2014APRS

description: The radio emission process in pulsars is not understood. However, pulsar timing is successfully used in extracting exciting science from radio pulsars and their environments. We are now approaching the point where the limits in our understanding of pulsar emission, affect the precision of pulsar timing experiments. State-switching and intermittent pulsars both show correlated changes between the shape of the average pulse profile and the spin-down rate, that is the first derivative of the rotational period. The spin-down rate changes have been shown to be related to changes in the torque imparted on the neutron star by magnetospheric currents. Our team have recently uncovered an extreme example of a state-switching pulsar in J0738-4042, adding to the small sample of known such sources, and showing that variability exists on a wide range of timescales. We now propose a further experiment to understand the connection between emission changes and timing irregularities. For this, we will target pulsars with well-known emission variability, which occurs on timescales of the order of the pulsar period, so-called moding and nulling. Using weekly monitoring, we will show whether there is a connection between the well known short timescale phenomena, and the long term intermittency and state-switching. Our ultimate objective is to understand what causes long-term variability in pulsars, be it an internal or external cause, and to include this knowledge in improved pulsar timing models.
DOI: 10.25919/5c36d96552f31
title: Parkes observations for project P875 semester 2014OCTS

description: In this proposal we aim to measure the flux densities of about 500 pulsars in a systematic way. Doing this we will vastly improve upon the current spectral knowledge of the pulsar population and provide around 1000 new measurements at 732 and 3100 MHz for about a quarter of the currently known pulsars. This is extremely important both for pulsar population studies, in particular for determining the spectral index of the radio emission, investigations of the pulsar emission mechanism and for optimizing observing strategies for next generation telescopes, such as the SKA. This proposal also acts as preparatory work for the Molonglo Observatory Synthesis Telescope, which we are refurbishing at the moment. This work will help us constrain the flux density expected at the centre frequency of Molonglo, at 843 MHz. We will also use the new data from Parkes to check and update the ephemerides of all the selected pulsars in order to have a database that is as up to date as possible before observations with Molonglo commence. We will also investigate the averaged pulse profiles and how they change with frequency.
DOI: 10.25919/5c36d96fef8df
title: Salisbury Stormwater Catchments

description: Polygon shapefile of catchment boundaries for stormwater harvesting schemes in Salisbury, South Australia
DOI: 10.25919/5c36d9788c02d
title: Biorig logger data

description: Biorig logger data for mains and stormwater rigs at Mawson Lakes site for the Infrastructure and Aesthetics component of the MARSUO Project.
DOI: 10.25919/5c36d9873084d
title: Coastal bank erosion data, Western Port Bay

description: This data was collected as part of a research project commissioned by Melbourne Water that investigates the rates and processes of coastal bank erosion in Western Port Bay, Victoria. Most of the timeseries data commences in Nov or Dec 2012 and extends to the date of upload (currently July 2013). The initial project ceased in December 2013. A phase 2 project continues until December 2016, and coastal bank erosion monitoring continues until ~June 2016 as part of that project.

The data includes: 
*GIS layers and air photos that were used to determine long term changes in the position of the coastline. The photos start from 1947 and cover an 8.6 km stretch of banks near Lang Lang.
*15-minute tide and groundwater peizometer data collected from the monitoring site near Lang Lang
*30-minute climate data recorded by the Davis weather station installed at the monitoring site.
*Photos of the site at high and low tide each day.

Further information on the data is provided in the supporting attachment (Information.pdf)
DOI: 10.25919/5c36d99e9e79e
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.
DOI: 10.25919/5c36d9b2b610c
title: Parkes observations for project P865 semester 2014APRS

description: In this pilot observing programme, we propose to observe at high cadence the transient gravitational-wave and radio-wave Universe.  The goals of these observations are threefold: 1) To improve the timing precision of secondary pulsars in the Parkes Pulsar Timing Array (PPTA) to accelerate the detection of gravitational waves; 2) To characterise the gravitational wave universe in the hitherto unexplored microhertz frequency band; and 3)  To develop methods and search for fast radio bursts (FRBs) while conducting precision time experiments. To achieve these goals, we request  120 hours of observations with the Parkes multibeam system, divided into 10 epochs comprising 12-hour LST days.     

This pilot project acts as a feasibility study for modifications to both the PPTA project and the International Pulsar Timing Array (IPTA), the consortium coordinating timing array observations in Australia, Europe, and North America, and assess the feasibility of searching for fast radio bursts while conduction precision timing observations.
DOI: 10.25919/5c36d9bbc6ce3
title: m_90mir85_snd: 9-second gridded continental Australia composite ecological change for Mammals 1990:2050 MIROC5 RCP 8.5 (CMIP5) (GDM: MAM_R2)

description: Composite ecological change as a function of three metrics (the potential degree of ecological change and of disappearing and novel ecological environments) shows where change might be greatest and different types of vulnerability using 30-year climate averages between the present (1990:1976- 2005) and projected future (2050:2036-2065) under the MIROC5 global climate model (RCP 8.5), based on a Generalised Dissimilarity Modelling (GDM) of compositional turnover for mammals (MAM_R2). 

Wherever the Potential degree of ecological change is scored low, ecological environments can neither be novel nor disappearing and minimal change is expected. But when the Potential degree of ecological change is scored high, a variety of possible types of change can occur depending on whether scores for Novel and/or Disappearing ecological environments are also high. 

To create a composite view, we assigned each of the three component measures to a colour band in a composite-band raster: local similarity as shades of green (inverted, 1-0 rescaled 0-255); novel as shades of blue (0-1 rescaled 0-255); and disappearing as shades of red (0-1 rescaled 0-255). The three layers can then be mapped simultaneously (red: band 3; green: band 1; blue: band 2) each scaled 0-255 to show the varying degrees of similar, novel and disappearing ecological environments and their combinations. 

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org. 

Data are provided as zipped ESRI tiff grids containing: raster image (*.tif) with associated header (*.tfw) and projection (*.xml) files. After extracting from the zip archive, these files can be imported into most GIS software packages. A readme file describes how to correctly reproduce the colour legend. In ArcGIS, the symbology statistics file can be used: "SND_display.stat.XML". 

Reproducing RGB composite colours for 3-band raster in ArcGIS: 
1. In file properties in ARCGIS, Symbology tab, Load XML "SND_display.stat.XML"
2. RED = BAND_3 (Disappearing)
3. GREEN = BAND_1 (Similarity )
4. BLUE = BAND_2 (Novel)
5. Always use min-max legend
6. Set each band in the custom range 0-255, mean = 126, std = 0

Layers in this 9s series use a consistent naming convention:
BIOLOGICAL GROUP _ FROM BASE TO SCENARIO _ ANALYSIS
e.g. A_90CAN85_SND or R_90MIR85_SND
where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants
and scenario is CAN: CanESM2; MIR: MIROC5
analysis, SND refers to – similarity, novel, disappearing