cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16926511.v1
title: Maximum Water Level S Scenario

description: Maximum water level at each node of the case study transect for the storm surge plus tide flooding (S) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926523.v1
title: Time to peak water level S Scenario

description: Time to peak water level at each node of the case study transect for the storm surge plus tide flooding (S) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926523
title: Time to peak water level S Scenario

description: Time to peak water level at each node of the case study transect for the storm surge plus tide flooding (S) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926532
title: Time to peak water level R Scenario

description: Time to peak water level at each node of the case study transect for the rainfall-runoff plus tides flooding (R) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926532.v1
title: Time to peak water level R Scenario

description: Time to peak water level at each node of the case study transect for the rainfall-runoff plus tides flooding (R) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926538.v1
title: Time to peak water level RS Scenario

description: Time to peak water level at each node of the case study transect for the combined flooding (RS) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.6084/m9.figshare.16926538
title: Time to peak water level RS Scenario

description: Time to peak water level at each node of the case study transect for the combined flooding (RS) scenario. Each row represents a different simulation and each column represents a node with the domain.
DOI: 10.5281/zenodo.5644201
title: Annotated data of simultaneous broadband radio and optical emission of meteor trains imaged by LOFAR / AARTFAAC and CAMS

description: This data set contains simultaneous 30 - 60 MHz LOFAR / AARTFAAC12 radio observations and CAMS low-light video observations of +4 to -10 magnitude meteors at the peak of the Perseid meteor shower on August 12/13, 2020. 204 meteor trains were imaged in both the radio and optical domain.
DOI: 10.15468/gpsr9n
title: A worldwide catalog of the Vermileonidae (Diptera: Brachycera)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Bueno, Gabriel M., Kehlmaier, Christian, Santos, Charles Morphy D. (2021): A worldwide catalog of the Vermileonidae (Diptera: Brachycera). Zootaxa 5060 (4): 489-514, DOI: https://doi.org/10.11646/zootaxa.5060.4.2
DOI: 10.5281/zenodo.5639253
title: ZeroCostDL4Mic / DeepBacs - Multi-label U-Net training dataset (Bacillus subtilis) and pretrained model

description: Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP, the manually annotated instance segmentation mask and the corresponding 2-label semantic segmentation mask used for model training. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads <strong>File format</strong>: .tiff (8-bit) <strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm) <strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin and mask images with labeled cytosol and cell boundaries were created using a custom Fiji macro (see our github repository). <strong>Multi-label U-Net model</strong>: The U-Net (2D) multilabel model was generated using the ZeroCostDL4Mic platform (Chamier & Laine et al., 2021). It was trained from scratch for 200 epochs on 733 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (256 x 256 px²)) with a batch size of 8 and a categorical_crossentrop loss function, using the U-Net (2D) multilabel ZeroCostDL4Mic notebook (v 1) (Chamier & Laine et al., 2021). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.1.105). The training was accelerated using a Tesla P100GPU. <strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.5281/zenodo.5639252
title: ZeroCostDL4Mic / DeepBacs - Multi-label U-Net training dataset (Bacillus subtilis) and pretrained model

description: Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP, the manually annotated instance segmentation mask and the corresponding 2-label semantic segmentation mask used for model training. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads <strong>File format</strong>: .tiff (8-bit) <strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm) <strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin and mask images with labeled cytosol and cell boundaries were created using a custom Fiji macro (see our github repository). <strong>Multi-label U-Net model</strong>: The U-Net (2D) multilabel model was generated using the ZeroCostDL4Mic platform (Chamier & Laine et al., 2021). It was trained from scratch for 200 epochs on 733 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (256 x 256 px²)) with a batch size of 8 and a categorical_crossentrop loss function, using the U-Net (2D) multilabel ZeroCostDL4Mic notebook (v 1) (Chamier & Laine et al., 2021). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.1.105). The training was accelerated using a Tesla P100GPU. <strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.6084/m9.figshare.16926580
title: Simulation Suit

description: File that contains the different environmental forcings parameters for each combination created using the Monte Carlo Simulation<br>
DOI: 10.6084/m9.figshare.16926580.v1
title: Simulation Suit

description: File that contains the different environmental forcings parameters for each combination created using the Monte Carlo Simulation<br>
DOI: 10.6084/m9.figshare.3761562.v2031
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.5550934
title: DeepBacs – Escherichia coli bright field segmentation dataset

description: Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation. Additional information can be found on this github wiki. The example shows a bright field image of live <em>E. coli </em>cells and the manually annotated segmentation mask. <strong>Data type</strong>: Paired bright field and segmented mask images <strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300). <strong>File format</strong>: .tif (8-bit) <strong>Image size</strong>: 1024 x 1024 px² (79 nm / pixel), 19/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) <strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512 x 512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. Two sets of mask images are provided: RoiMaps for instance segmentation using e.g. StarDist or binary images for CARE or U-Net. <br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.5550933
title: DeepBacs – Staphylococcus aureus widefield segmentation dataset

description: Training and test images of live <em>S. aureus</em> cells for the task of cell segmentation. Additional information can be found in the github wiki. The example shows the bright field and Nile Red fluorescence image of live <em>S. aureus </em>cells, as well as the manually annotated segmentation mask. <strong>Data type</strong>: Paired DIC/fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (DIC and fluorescence) <strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) <strong>Cell type</strong>: <em>S. aureus</em> strain JE2 grown under agarose pads <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512 x 512 px² (80 nm/px)<br> <strong>Image preprocessing</strong>: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches. <strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup> <strong>Contact email</strong>: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt <strong>Affiliation</strong>: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842
DOI: 10.5281/zenodo.5550932
title: DeepBacs – Staphylococcus aureus widefield segmentation dataset

description: Training and test images of live <em>S. aureus</em> cells for the task of cell segmentation. Additional information can be found in the github wiki. The example shows the bright field and Nile Red fluorescence image of live <em>S. aureus </em>cells, as well as the manually annotated segmentation mask. <strong>Data type</strong>: Paired DIC/fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (DIC and fluorescence) <strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) <strong>Cell type</strong>: <em>S. aureus</em> strain JE2 grown under agarose pads <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 512 x 512 px² (80 nm/px)<br> <strong>Image preprocessing</strong>: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches. <strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup> <strong>Contact email</strong>: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt <strong>Affiliation</strong>: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842
DOI: 10.5281/zenodo.5550968
title: DeepBacs – Bacillus subtilis fluorescence segmentation dataset

description: Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP and the manually annotated segmentation mask. <strong>Data type</strong>: Paired fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads <strong>File format</strong>: .tiff (8-bit) or .png (8-bit) For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models <strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm) <strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin <strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.5281/zenodo.5550967
title: DeepBacs – Bacillus subtilis fluorescence segmentation dataset

description: Training and test images of live <em>B. subtilis </em>cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live <em>B. subtilis </em>cells expressing FtsZ-GFP and the manually annotated segmentation mask. <strong>Data type</strong>: Paired fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads <strong>File format</strong>: .tiff (8-bit) or .png (8-bit) For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models <strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm) <strong>Image preprocessing</strong>: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin <strong>Author(s)</strong>: Mia Conduit<sup>1,2</sup>, Séamus Holden<sup>1,3</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.5519/qd.j90d34fv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.18112/openneuro.ds002578.v1.1.0
title: Visual Oddball Task (256 channels)
DOI: 10.3886/e146344v1-100602
title: Medical Expenditure Panel Survey (MEPS) Data, 1996-2017

description: The Medical Expenditure Panel Survey, which began in 1996, is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers.<br><br>The files in this deposit were downloaded from the AHRQ website by Julia Dennett, Yale University, and Toby Chaiken, J-PAL North America, and archived by Travis Donahoe, Harvard University. Additional information edited by Michael Darisse and Lars Vilhuber, Cornell University and American Economic Association.<br><br><br>
DOI: 10.5281/zenodo.5551016
title: DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model

description: Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model. Additional information can be found on this github wiki. The example shows a bright field image of live <em>E. coli</em> cells and the respective annotation for specific growth stages. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (bright field) and annotations in PASCAL VOC format <strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300). <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) <strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using <em>LabelImg </em>(https://github.com/tzutalin/labelImg). <strong>YOLOv2 model</strong> The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.5551015
title: DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model

description: Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model. Additional information can be found on this github wiki. The example shows a bright field image of live <em>E. coli</em> cells and the respective annotation for specific growth stages. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (bright field) and annotations in PASCAL VOC format <strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300). <strong>File format</strong>: .png (8-bit) <strong>Image size</strong>: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset) 1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series) <strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using <em>LabelImg </em>(https://github.com/tzutalin/labelImg). <strong>YOLOv2 model</strong> The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578