Iris

## Overview

Iris is a dataset for object detection tasks - it contains Iris annotations for 444 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Iris Species Dataset

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 dataset is taken from UCI Machine Learning Repository's… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/iris.

Iris_seg

## Overview

Iris_seg is a dataset for instance segmentation tasks - it contains Iris annotations for 417 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Detect Iris

## Overview

Detect Iris is a dataset for computer vision tasks - it contains Iris annotations for 508 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat. The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.

Generalisation of the limits provided by INSEE for the return of statistical data on an infra-communal scale, the Francisian extraction of the IRIS perimeters (from the product Contours...Iris® distributed by the IGN) covers all the municipalities of Ile-de-France.

Municipalities with at least 10 000 inhabitants and most municipalities with 5,000 to 10 000 inhabitants are divided into IRIS. This division, which is the basis for the dissemination of sub-communal statistics, constitutes a partition of the territory of these communes into “neighbourhoods” with a population of about 2,000 inhabitants. By extension, in order to cover the whole territory, each of the municipalities not divided into IRIS is treated as an IRIS.

This division was drawn up in partnership with local partners, in particular the municipalities, in accordance with precise rules defined in consultation with the Commission Nationale Informatique et Libertés (CNIL). It is constructed on the basis of geographical and statistical criteria and, as far as possible, each IRIS must be homogeneous in terms of habitat. The IRIS offer the most developed tool to date to describe the internal structure of nearly 1,900 municipalities with at least 5,000 inhabitants.

Circles proportional to the 2011 population located in the centre of the IRIS of Ile-de-France and associating variables from the 2011 population census. Confined to the limits of their original IRISs, these abstract cartographic objects visually reflect information more rooted in the reality of their demography and can be used as a medium for thematic analysis of other information derived from the data awarded to this population and expressed in rates.

Sonification for Exploratory Data Analysis #### Chapter 8: Sonification Models In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data. ##### 8.1 Data Sonograms Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space. * Table 8.2, page 87: Sound examples for Data Sonograms File: Iris dataset: started in plot (a) at S0 (b) at S1 (c) at S2
10d noisy circle dataset: started in plot (c) at S0 (mean) (d) at S1 (edge)
10d Gaussian: plot (d) started at S0
3 clusters: Example 1
3 clusters: invisible columns used as output variables: Example 2 Description: Data Sonogram Sound examples for synthetic datasets and the Iris dataset Duration: about 5 s ##### 8.2 Particle Trajectory Sonification Model This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset. * Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x). * Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters. * Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster. * Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters * Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster * Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step. * Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step. * Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset. ##### 8.3 Markov chain Monte Carlo Sonification The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound. * Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes. * Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset * McMC Sonification for Cluster Analysis, dataset with three clusters, page 107 * Stream 1 MCMC-Ex-3.1 * Stream 2 MCMC-Ex-3.2 * Stream 3 MCMC-Ex-3.3 * Mix MCMC-Ex-3.4 * McMC Sonification for Cluster Analysis, dataset with three clusters, T =0.002s, page 107 * Stream 1 MCMC-Ex-4.1 (stream 1) * Stream 2 MCMC-Ex-4.2 (stream 2) * Stream 3 MCMC-Ex-4.3 (stream 3) * Mix MCMC-Ex-4.4 * McMC Sonification for Cluster Analysis, density with 6 modes, T=0.008s, page 107 * Stream 1 MCMC-Ex-5.1 (stream 1) * Stream 2 MCMC-Ex-5.2 (stream 2) * Stream 3 MCMC-Ex-5.3 (stream 3) * Mix MCMC-Ex-5.4 * McMC Sonification for the Iris dataset, page 108 * MCMC-Ex-6.1 * MCMC-Ex-6.2 * MCMC-Ex-6.3 * MCMC-Ex-6.4 * MCMC-Ex-6.5 * MCMC-Ex-6.6 * MCMC-Ex-6.7 * MCMC-Ex-6.8 ##### 8.4 Principal Curve Sonification Principal Curve Sonification represents data by synthesizing the soundscape while a virtual listener moves along the principal curve of the dataset through the model space. * Noisy Spiral dataset, PCS-Ex-1.1 , page 113 * Noisy Spiral dataset with variance modulation PCS-Ex-1.2 , page 114 * 9d tetrahedron cluster dataset (10 clusters) PCS-Ex-2 , page 114 * Iris dataset, class label used as pitch of auditory grains PCS-Ex-3 , page 114 ##### 8.5 Data Crystallization Sonification Model * Table 8.6, page 122: Sound examples for Crystallization Sonification for 5d Gaussian distribution File: DCS started at center, in tail, from far outside Description: DCS for dataset sampled from N{0, I_5} excited at different locations Duration: 1.4 s * Mixture of 2 Gaussians, page 122 * DCS started at point A DCS-Ex1A * DCS started at point B DCS-Ex1B * Table 8.7, page 124: Sound examples for DCS on variation of the harmonics factor File: h_omega = 1, 2, 3, 4, 5, 6 Description: DCS for a mixture of two Gaussians with varying harmonics factor Duration: 1.4 s * Table 8.8, page 124: Sound examples for DCS on variation of the energy decay time File: tau_(1/2) = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2 Description: DCS for a mixture of two Gaussians varying the energy decay time tau_(1/2) Duration: 1.4 s * Table 8.9, page 125: Sound examples for DCS on variation of the sonification time File: T = 0.2, 0.5, 1, 2, 4, 8 Description: DCS for a mixture of two Gaussians on varying the duration T Duration: 0.2s -- 8s * Table 8.10, page 125: Sound examples for DCS on variation of model space dimension File: selected columns of the dataset: (x0) (x0,x1) (x0,...,x2) (x0,...,x3) (x0,...,x4) (x0,...,x5) Description: DCS for a mixture of two Gaussians varying the dimension Duration: 1.4 s * Table 8.11, page 126: Sound examples for DCS for different excitation locations File: starting point: C0, C1, C2 Description: DCS for a mixture of three Gaussians in 10d space with different rank(S) = {2,4,8} Duration: 1.9 s * Table 8.12, page 126: Sound examples for DCS for the mixture of a 2d distribution and a 5d cluster File: condensation nucleus in (x0,x1)-plane at: (-6,0)=C1, (-3,0)=C2, ( 0,0)=C0 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s * Table 8.13, page 127: Sound examples for DCS for the cancer dataset File: condensation nucleus in (x0,x1)-plane at: benign 1, benign 2
malignant 1, malignant 2 Description: DCS for a mixture of a uniform 2d and a 5d Gaussian Duration: 2.16 s ##### 8.6 Growing Neural Gas Sonification * Table 8.14, page 133: Sound examples for GNGS Probing File: Cluster C0 (2d): a, b, c
Cluster C1 (4d): a, b, c
Cluster C2 (8d): a, b, c Description: GNGS for a mixture of 3 Gaussians in 10d space Duration: 1 s * Table 8.15, page 134: Sound examples for GNGS for the noisy spiral dataset File: (a) GNG with 3 neurons 1, 2
(b) GNG with 20 neurons end, middle, inner end
(c) GNG with 45 neurons outer end, middle, close to inner end, at inner end
(d) GNG with 150 neurons outer end, in the middle, inner end
(e) GNG with 20 neurons outer end, in the middle, inner end
(f) GNG with 45 neurons outer end, in the middle, inner end Description: GNG probing sonification for 2d noisy spiral dataset Duration: 1 s * Table 8.16, page 136: Sound examples for GNG Process Monitoring Sonification for different data distributions File: Noisy spiral with 1 rotation: sound
Noisy spiral with 2 rotations: sound
Gaussian in 5d: sound
Mixture of 5d and 2d distributions: sound Description: GNG process sonification examples Duration: 5 s #### Chapter 9: Extensions #### In this chapter, two extensions for Parameter Mapping

Seismometers were placed on a 25 km by 50 km iceberg called C16 in the Ross Sea, Antarctica, to identify the Iceberg harmonic Tremor (IHT) source mechanism and to understand the relevance of IHT to iceberg calving, drift and break-up. The seismic observations reveal that the IHT signal consists of extended episodes of stick-slip icequakes (typically thousands per hour) generated when the ice-cliff edges of two tabular icebergs rub together during glancing, strike/slip type iceberg collisions (e.g., between C16 and B15A). With the source mechanism revealed, IHT may provide a promising signal useful for the study of iceberg behavior and iceberg-related processes such as climate-induced ice-shelf disintegration.

Here, a single day of seismometer data for a single station on iceberg C16 is provided as an example of "a day in the life of an iceberg" for use by scientists and students wishing to know more about IHT. The station data is from C16 "B" site on C16's northeast corner, and the day is 27 December, 2003, a day when B15A struck C16 and caused an episode of tremor that was particularly easy to identify and understand.

This represents only a small fraction of the total data that exist for the seismic program on iceberg C16. The full data are archived at the IRIS data center (where seismic data is commonly archived). This one-day data set is to provide glaciologists with ready access to a good example of IHT that they can use for teaching and for demonstration purposes. Data are available in comma-delimited ASCII format and Matlab native mat files. Data are available via FTP.

The second version of the published dataset contains 264 relevés (descriptions of vegetation plots) in the habitats of Iris aphylla L. located in the European part of Russia. The dataset includes 14003 records indicating presence of a plant taxon (species or other) on a plot. Data are entered into GBIF for the first time in this dataset. 120 descriptions are published for the first time in GBIF. 144 descriptions have been published earlier on paper (Kovyly..., 2015; Averinova et al., 2021).

Description

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, a system that combines 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. The dataset is already available here: https://shorturl.at/cgjtz. Check out our website https://cloudsen12.github.io/ for examples of how to download the dataset via STAC.

This dataset, built from INSEE, IGN and departmental data, describes different administrative and social perimeters in the Seine-Maritime Department in 2022. There are four types of administrative perimeter: IRIS (Grouped Islands for Statistical Information). This division, set up by INSEE, is the basic building block for the dissemination of infra-municipal data. An IRIS represents between 1,800 and 5,000 inhabitants or more than 1,000 employees or a specific sparsely populated right-of-way (port, nature park, etc.), the municipalities, inter-municipalities (Public Establishments of Inter-municipal Cooperation or EPCI), which are groupings of municipalities around common projects: Communities of Municipalities, Agglomeration Communities, Metropolises, etc., the cantons, which are the constituencies serving as the framework for the election of departmental councillors, as well as three sectorisations corresponding to the scales of implementation of the Department’s social policies: the sectors of the Medical and Social Centres (CMS), departmental structures that provide medical follow-up for babies and young children but also constitute a local entry point for access to rights (professional integration, support for the elderly, etc.) groupings of CMS, which are more technical perimeters used by the Department for the implementation of its social policies, and the Territorial Units of Social Action (UTAS), the Department is divided into 5 UTAS, which are both places of reception, information, guidance and accompaniment of the public as well as perimeters of reflection with sociological and territorial specificities. This information is also available on the Opendata76 website in the form of an interactive map allowing the visualization of the perimeters. Metadata Link to metadata Additional resources INSEE website: https://www.insee.fr/en/home The website of the National Institute of Statistics and Economic Studies provides detailed definitions of the different French administrative perimeters and also allows you to download many data at these scales. Geoservices website:https://geoservices.ign.fr/ Many data from the National Institute of Geographical and Forestry Information are freely downloadable, in particular in shape format, on this site published by IGN. Website of the Seine-Maritime Department: https://www.seinemaritime.fr/my-daily/health/the-medico-social-centres-.html and https://www.seinemaritime.fr/my-department/the-territory/the-utas.html The website of the Department of Seine-Maritime provides more information on the role of CMS and UTAS and the services they may offer.

This repository contains training and validation data for

HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology

(Under review at MIDL2024)

More information and code are available at https://github.com/digitalpathologybern/hover_next_inference

Modified Lizard dataset to include mitosis (lizard_mitosis.zip), mitosis dataset (mitosis_ds.zip) and a holdout eosinophil validation set (eos_eval.zip)

mitosis_ds.zip also contains the hold-out H&E mitosis test set.

The original lizard dataset was createdy by Simon Graham et al. and was shared under CC BY-NC-SA 4.0. The tile-based dataset can be downloaded from https://conic-challenge.grand-challenge.org/Data/ after registering for the challenge. We modify the dataset by including an additional mitosis class, however note that there are a number of mitosis which are still not (correctly annotated).

This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).

In this repo, only data from the S2 tile T30UVU are available. To download the full pretraining dataset, see: 10.5281/zenodo.7891924

This dataset provides the distributional data on juno irises (Iris sect. Juno) in Central Asia. The study is focused on Central Asia but not limited to that territory, to include the neighbouring countries when the species distributions extend outside.

At present the dataset is limited to two species, Iris bucharica and I. orchioides. The data collection will continue whenever possible.

Main data files comprise 22 variables in three subcategories of risk (political, financial, and economic) for 146 countries for 1984-2021. Data are annual averages of the components of the ICRG Risk Ratings (Tables 3B, 4B, and 5B) published in the International Country Risk Guide. Indices include: political: government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religion in politics, law and order, ethnic tensions, democratic accountability, and bureaucratic quality; financial: foreign debt, exchange rate stability, debt service, current account, international liquidity; and economic: inflation, GDP per head, GDP growth, budget balance, current account as % of GDP. Table 2B provides annual averages of the composite risk rating. Table 3Ba provides historical political risk subcomponents on a monthly basis from May 2001-February 2022. Also includes the IRIS-3 dataset by Steve Knack and Philip Keefer, which covers the period of 1982-1997 and computed scores for six additional political risk variables: corruption in government, rule of law, bureaucratic quality, ethnic tensions, repudiation of contracts by government, and risk of expropriation. Additional data files provide country risk ratings and databanks (economic and social indicators) for new emerging markets for 2000-2009.

Summary tabular data relating to Natura 2000 SAC sites in Ireland, providing Natura 2000 site-related details, including lists of the habitats and species listed in Annex I and Annex II of the Habitats Directive for which each Natura 2000 site is selected. Data is accurate up to March 2023. Please check the Iris Oifigiúil, Irish, Irish Statute Book for more recently published Statutory Instrument (S.I.) regulations. Data is provided in a single zip file containing sub folders holding MS Excel, CSV and JSON formats, each accompanied by a ‘readme’ file. This data should be read in conjunction with the spatial (GIS) boundaries for sites, site documents and related publications (see further https://www.npws.ie/maps-and-data/designated-site-data/ )

P_iris

## Overview

P_iris is a dataset for object detection tasks - it contains Maculas annotations for 479 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A heat network is a centrally produced heat distribution system that serves a large number of users (public or private tertiary buildings, condominiums, social housing, etc.). One of the major assets of the heat networks is to mobilise renewable energy present in the territory, which is difficult to distribute otherwise. Data at IRIS on consumption and delivery points on the Francisian networks allow an understanding of the degree of operation of the current networks and gives an overview of the areas in which connections would benefit from multiplication.