The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".

CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:- clear (0%)- low-cloudy (1% - 25%) - almost clear (25% - 45%)- mid-cloudy (45% - 65%)- cloudy (65% >)An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).2000 ROIs with scribble-level annotation, where the annotation time is 15 minutes (scribble group).5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, combining human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no-annotation dataset is generated automatically, with manual annotation only in the clear image patch. A backup of the dataset in STAC format is available here: https://shorturl.at/cgjtz. Check out our website https://cloudsen12.github.io/ for examples.

This paper presents a novel approach for the interactive optimization of sonification parameters. In a closed loop, the system automatically generates modified versions of an initial (or previously selected) sonification via gradient ascend or evolutionary algorithms. The human listener directs the optimization process by providing relevance feedback about the perceptual quality of these propositions. In summary, the scheme allows users to bring in their perceptual capabilities without burdening them with computational tasks. It also allows for continuous update of exploration goals in the course of an exploration task. Finally, Interactive Optimization is a promising novel paradigm for solving the mapping problems and for a user-centred design of auditory display. The paper gives a full account on the technique, and demonstrates the optimization at hand of synthetic and real-world data sets. ### Sonification examples Fisher Data set (two overlapping clusters): The sonification examples are the parents of a series of optimization steps, starting with a random sonification and optimizing towards an audible separation of chlusters. SFk is the parent of iteration k. + SF1 (mp3, 28k) + SF3 (mp3, 28k) + SF5 (mp3, 28k) + SF6 (mp3, 28k) + SF7 (mp3, 28k) + SF9 (mp3, 28k) + SF10 (mp3, 28k) good audible clustering structure + SF11 (mp3, 28k) + SF12 (mp3, 28k) + SF13 (mp3, 28k) + SF14 (mp3, 28k) + SF15 (mp3, 28k) + SF17 (mp3, 28k) + SF18 (mp3, 28k) + SF19 (mp3, 28k) + SF20 (mp3, 28k) + SF21 (mp3, 28k) + SF22 (mp3, 28k) + SF23 (mp3, 28k) + SF24 (mp3, 28k) + SF25 (mp3, 28k) + SF33 (mp3, 28k) + SF34 (mp3, 28k) + SF37 (mp3, 28k) Iris data set (4d data with 3 clusters, 150 items): The sonification examples are again the parents for the successive iterations during evolutionary optimization SIk denotes (S)ound Example for (I)ris data set iteration k. + SI1 (mp3, 40k) + SI2 (mp3, 40k) + SI3 (mp3, 40k) + SI4 (mp3, 40k) + SI5 (mp3, 40k) + SI6 (mp3, 40k) + SI7 (mp3, 40k) + SI8 (mp3, 40k) + SI9 (mp3, 40k) + SI10 (mp3, 40k) The following examples are optimization results for increased attention to amplitude and panning. + SI Amplitude (mp3, 40k) + SI Panning (mp3, 40k) ### SuperCollider Extensions Find here SuperCollider classes by Thomas Hermann, which are useful for programming sonifications. #### OctaveSC OctaveSC is a class to interface with the free powerful math package octave. Description The class allows to call octave functions and execute octave instructions from sc3 transfer data between octave and sc3 use the SuperCollider rtf document editor as octave shell (tested on OSX): via CTRL-RETURN the current line or selection can be executed in octave. This allows to interleave octave code and explaning text in the same way as it can be done with sc code. #### Download OctaveSC is provided as zip archive (download (OctaveSC.zip (16kB) ]) with the OctaveSC class directory containing the class file OctaveSC.sc and a help file OctaveSC.help.rtf. See the README.txt for installation instructions and how to get started. #### Contributing to OctaveSC The standard data types (scalars, vectors, matrices) of numbers work reliable, and I find OctaveSC very useful. However, its functionality is far from complete in this version. In particular, I'd like to address for future versions the integration of high-level commands to exchange strings exchange of string matrices checks for proper dimensions when exchanging matrices and vectors automatic matrix-2-vector conversion for Nx1 matrices (which currently appear in sc as arrays of arrays with 1 element each) working with structures. Suggestions for improving OctaveSC are very welcome, please e-mail your code fragment for inclusion into the official distribution provided on this website.

This layer comprises road centrelines for all roads controlled by Main Roads (State Roads) and all roads controlled by Local Government (Local Roads) that are assigned road numbers in the state of Western Australia. Other centreline is also included for paths and unknown roads.The Road Network are attributed with a road number as identified in Main Roads’ corporate Integrated Road Information System (IRIS) and the local government Road Management database (ROMAN). The purpose of this layer is to identify roads as identified in IRIS.Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/