Map 6 "Biotopes, Flora" of the LRP Teltow-Fläming. (Standing waters, anthropogenic raw soil sites and ruderal areas, grass and perennial meadows, dwarf shrub heaths, forests and forests, fields, biotopes of greenery and open spaces, built-up areas, traffic facilities, special areas)

COCO is a large-scale object detection, segmentation, and captioning dataset.

Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). * Coco defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('coco', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/coco-2014-1.1.0.png" alt="Visualization" width="500px">

The comparison of time-consuming in WMD and fast WMD.

Application: Single-stage Object Detection Base model: MobileNet-v1 Framework: tensorflow1.1 Training Information: weights were fine-tuned using TF fake quantization nodes Quality: The COCO mAP(IoU=0.50:0.95) on 5000 validation images is 23.0% Precision: 8-bit precision Is Quantized: Yes, using fake quantization - i.e., weights appear in float32 but have only 256 unique values. Dataset: COCO val-2017

This digital data set contains geologic formations for the 1:250,000-scale Sherman quadrangle, Texas and Oklahoma. The original data are from the Bureau of Economic Geology publication, "Geologic Atlas of Texas, Sherman sheet", by J.H. McGowen, T.F. Hentz, D.E. Owen, M.K. Pieper, C.A. Shelby, and V.E. Barnes, 1967, revised 1991.

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures.

This dataset features a detailed map of material stocks for the whole of Austria on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Temporal extent The map is representative for ca. 2018.

Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

The dataset features

area and mass for different street types

area and mass for different rail types

area and mass for other infrastructure

area, volume and mass for different building types

Masses are reported as total values, and per material category.

Units

area in m²

height in m

volume in m³

mass in t for infrastructure and buildings

Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

This dataset includes input data used in the following article: Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard. 2018. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar. Biological Conservation. 222: 189-197. [doi:10.1016/j.biocon.2018.04.008]. bioRxiv: 147827. Results from this article have been updated for the periods 2010-2015 and 2015-2017.

Description

Dataset of TFRecords files made from Plant Pathology 2021 original competition data. Changes: * labels column of the initial train.csv DataFrame was binarized to multi-label format columns: complex, frog_eye_leaf_spot, healthy, powdery_mildew, rust, and scab * images were scaled to 512x512 * 77 duplicate images having different labels were removed (see the context in this notebook) * samples were stratified and split into 5 folds (see corresponding folders fold_0:fold_4) * images were heavily augmented with albumentations library (for raw images see this dataset) * each folder contains 5 copies of randomly augmented initial images (so that the model never meets the same images)

I suggest adding all 5 datasets to your notebook: 4 augmented datasets = 20 epochs of unique images (1, 2, 3, 4) + 1 raw dataset for validation here.

For a complete example see my TPU Training Notebook

Contents:

• preprocessed DataFrame train.csv
• fold indexes DataFrame folds.csv
• fold_0:fold_4 folders containing 64 .tfrec files, respectively, with feature map shown below: feature_map = { 'image': tf.io.FixedLenFeature([], tf.string), 'name': tf.io.FixedLenFeature([], tf.string), 'complex': tf.io.FixedLenFeature([], tf.int64), 'frog_eye_leaf_spot': tf.io.FixedLenFeature([], tf.int64), 'healthy': tf.io.FixedLenFeature([], tf.int64), 'powdery_mildew': tf.io.FixedLenFeature([], tf.int64), 'rust': tf.io.FixedLenFeature([], tf.int64), 'scab': tf.io.FixedLenFeature([], tf.int64)} ### Acknowledgements
• photo from Unsplash here

Pointed representation of the sources from map 13 "surface waters" of the LRP Teltow-Fläming.

The results of experiments on Ren_CECps and 20 newsgroup.

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures.

This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Temporal extent The map is representative for ca. 2018.

Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

The dataset features

area and mass for different street types

area and mass for different rail types

area and mass for other infrastructure

area, volume and mass for different building types

Masses are reported as total values, and per material category.

Units

area in m²

height in m

volume in m³

mass in t for infrastructure and buildings

Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

Beschrijving van de INSPIRE Download Service (vooraf gedefinieerde Atom): Wijziging van de TF - De link(s) voor het downloaden van de records wordt/worden dynamisch gegenereerd vanuit Get Map calls naar een WMS interface Beschrijving van de INSPIRE Download Service (vooraf gedefinieerde Atom): Wijziging van de TF - De link(s) voor het downloaden van de records wordt/worden dynamisch gegenereerd vanuit Get Map calls naar een WMS interface Beschrijving van de INSPIRE Download Service (vooraf gedefinieerde Atom): Wijziging van de TF - De link(s) voor het downloaden van de records wordt/worden dynamisch gegenereerd vanuit Get Map calls naar een WMS interface

Abstract

Part 3 of 3 of the Liver Tumor Segmentation Challenge Data.

To participate in the challenge look at LiTS competition website

For info on TPU setup please look at the documentation

TFRecord content

Please look at the starter notebook for below steps

The bucket path: python from kaggle_datasets import KaggleDatasets GCS_PATH = KaggleDatasets().get_gcs_path('liver-tumor-segmentation-in-tfrecords-part-3') paths = tf.io.gfile.glob(f"{GCS_PATH}/*")

The TFRecord reader: ```python def read_tfrecord(serialized_example): """ Reads a serialized tf.Example message from a google storage bucket. """ feature_description = {'example_id': tf.io.FixedLenFeature([1], tf.int64), 'shape': tf.io.FixedLenFeature([3], tf.int64), 'volume': tf.io.FixedLenFeature([], tf.string), 'segmentation': tf.io.FixedLenFeature([], tf.string)}

example = tf.io.parse_single_example(serialized_example, feature_description)
volume = tf.io.parse_tensor(example['volume'], tf.int16)
volume.set_shape((None, None, None))
segmentation = tf.io.parse_tensor(example['segmentation'], tf.uint8)
segmentation.set_shape((None, None, None))

return example['example_id'], example['shape'], volume, segmentation

Initilize dataset:
```python
raw_train_dataset = tf.data.TFRecordDataset(filenames=paths, compression_type='GZIP')
raw_train_dataset = raw_train_dataset.map(read_tfrecord)

Organizer and Data Contributors

Technical University of Munich

Patrick Christ, Florian Ettlinger, Felix Gruen, Sebastian Schlecht, Jana Lipkova, Georgios Kassis, Sebastian Ziegelmayer, Fabian Lohöfer, Rickmer Braren & Bjoern Menze Ludwig Maxmilian University of Munich

Julian Holch, Felix Hofmann, Wieland Sommer & Volker Heinemann Radboudumc

Colin Jacobs, Gabriel Efrain HumpireMamani & Bram van Ginneken Polytechnique Montréal & CHUM Research Center

Gabriel Chartrand, Eugene Vorontsov, An Tang, Michal Drozdzal & Samuel Kadoury Tel Aviv University & Sheba Medical Center

Avi Ben-Cohen, Eyal Klang, Marianne M. Amitai, Eli Konen & Hayit Greenspan. IRCAD

Johan Moreau, Alexandre Hostettler & Luc Soler The Hebrew University of Jerusalem & Hadassah University Medical Center

Refael Vivanti, Adi Szeskin, Naama Lev-Cohain, Jacob Sosna & Leo Joskowicz Special thanks to the CodaLab Team for helping us

Eric Carmichael & Flavio Alexander

Attribute

@misc{bilic2019liver, title={The Liver Tumor Segmentation Benchmark (LiTS)}, author={Patrick Bilic and Patrick Ferdinand Christ and Eugene Vorontsov and Grzegorz Chlebus and Hao Chen and Qi Dou and Chi-Wing Fu and Xiao Han and Pheng-Ann Heng and Jürgen Hesser and Samuel Kadoury and Tomasz Konopczynski and Miao Le and Chunming Li and Xiaomeng Li and Jana Lipkovà and John Lowengrub and Hans Meine and Jan Hendrik Moltz and Chris Pal and Marie Piraud and Xiaojuan Qi and Jin Qi and Markus Rempfler and Karsten Roth and Andrea Schenk and Anjany Sekuboyina and Eugene Vorontsov and Ping Zhou and Christian Hülsemeyer and Marcel Beetz and Florian Ettlinger and Felix Gruen and Georgios Kaissis and Fabian Lohöfer and Rickmer Braren and Julian Holch and Felix Hofmann and Wieland Sommer and Volker Heinemann and Colin Jacobs and Gabriel Efrain Humpire Mamani and Bram van Ginneken and Gabriel Chartrand and An Tang and Michal Drozdzal and Avi Ben-Cohen and Eyal Klang and Marianne M. Amitai and Eli Konen and Hayit Greenspan and Johan Moreau and Alexandre Hostettler and Luc Soler and Refael Vivanti and Adi Szeskin and Naama Lev-Cohain and Jacob Sosna and Leo Joskowicz and Bjoern H. Menze}, year={2019}, eprint={1901.04056}, archivePrefix={arXiv}, primaryClass={cs.CV} }

Summary of phosphoproteins and phosphosites determined by MS analysis.

TEC database provides the regulatory targets for all sigma factors and transcription factor (TF)s in E. coli identified by genomic SELEX screening. Users can search either TFs that may regulate specific genes or target genes regulated by TF(s) of specific interest. Then, the results can be filtered by binding intensity and/or location. Users can view both bar chart and heat map of TF binding, and can analyze consensus sequences and download raw data.

This repository includes input data used in the following article:

Vieilledent G., C. Grinand, F. A. Rakotomalala, R. Ranaivosoa, J.-R. Rakotoarijaona, T. F. Allnutt, and F. Achard. Combining global tree cover loss data with historical national forest-cover maps to look at six decades of deforestation and forest fragmentation in Madagascar.

For this article, data have been processed with a R/GRASS script. The development version of this script is available on GitHub at https://github.com/ghislainv/deforestation-maps-Mada. The last release of this script is archived on Zenodo: [DOI: 10.5281/zenodo.1118484].

Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets. Overall, we provide evidence that these datasets are an invaluable regulatory catalogue that can be used to drive a systems understanding of MTB pathogenicity.

Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.

Additional file 3. Statements included in each concept map category.

Summary of the evidence for PTMs in SCRTT.