cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7807961
title: Microservice Anti-Patterns and Bad Smells: A Tertiary Study

description: This dataset contains the search and classification processes of anti-patterns and bad smells catalog tertiary study.
DOI: 10.5281/zenodo.7807962
title: Microservice Anti-Patterns and Bad Smells: A Tertiary Study

description: This dataset contains the search and classification processes of anti-patterns and bad smells catalog tertiary study.
DOI: 10.5281/zenodo.7993516
title: Microservice Anti-Patterns and Bad Smells: A Tertiary Study

description: This dataset contains the search and classification processes of anti-patterns and bad smells catalog tertiary study.
DOI: 10.6084/m9.figshare.23275193
title: GAF-QCNN

description: as read me.txt
DOI: 10.25625/ytejkq/cijqmx
title: 19f-main.tar

description: :unav
DOI: 10.25625/ytejkq
title: Code and Experimental data for: "Bayesian Optimization to Estimate Hyperﬁne Couplings from 19F ENDOR Spectra"

description: Complete simulation and optimization code and raw data used for the Paper: "Bayesian Optimization to Estimate Hyperﬁne Couplings from 19F ENDOR Spectra"
DOI: 10.26050/wdcc/pmmxmcrtdgp111
title: PalMod2 MPI-M MPI-ESM1-2-CR Transient Simulations of the Last Deglaciation with prescribed ice sheets from GLAC-1D reconstructions (r1i1p1f1)

description: Project: PalMod2 - From the last interglacial to the anthropocene - modeling a complete glacial cycle. The PalMod project (https://www.palmod.de/) aims for a better understanding of how slow feedbacks in the Earth system operate. Using the new insights into Earth system dynamics, PalMod will also make projections of the future climate over the next few millennia. The project PalMod is in the second phase and funded by the German Federal Ministry of Education and Research as a Research for Sustainability Initiative. Summary: The transient simulation was performed with the Max Planck Institute for Meteorology Earth System Model version 1.2 in coarse resolution (MPI-ESM-CR). The model includes the spectral atmospheric model ECHAM6.3 at T31 horizontal resolution (approx. 3.75°) and 31 vertical levels, the land surface vegetation model JSBACH3.2, and the primitive equation ocean model MPIOM1.6 with a nominal resolution of 3°. The applied setup was introduced in detail in Kapsch et al. (2021 and 2022). For the experiment, the model was integrated from a glacial state at 26 ka to the year 1950 with prescribed atmospheric greenhouse gas concentrations (Köhler et al., 2017) and insolation (Berger & Loutre, 1991). Ice sheets and surface topographies were prescribed from GLAC-1D (Tarasov et al., 2012) reconstructions. All forcing fields are updated every 10 years of the simulations and initiate changes in the topography, glacier mask, river pathways, ocean bathymetry, and land-sea mask. Meltwater from ice sheets is calculated as the temporal derivative of ice thickness at grid points covered by grounded ice sheets. The derived meltwater is then distributed by the hydrological discharge model and finally released into the ocean as freshwater. The experiment was performed as part of a model ensemble that contains simulations differing in terms of their tuning parameters, parameterizations and bug fixes. The ensemble is described in detail in the Supplementary Material of Kapsch et al. (2022). The current simulation refers to GLAC1D_P1 in Kapsch et al. (2022) and is significantly warmer during the glacial than model versions P2 and P3, indicating a weaker sensitivity to greenhouse gas changes. The glacial cooling over the North Atlantic is more realistic in P2 and P3 than in P1. P2 and P3 are also more sensitive to small changes in the meltwater forcing than P1. The experiment was performed as part of a model ensemble that contains simulations differing in terms of their tuning parameters, parameterizations, bug fixes and forcing. These differences are described according to the CMIP6 nomenclature, where r denotes the realization, i the initialization method, p differences in the physics and f in the forcing. The ensemble is described in detail in the Supplementary Material of Kapsch et al. (2022).
DOI: 10.34740/kaggle/dsv/5823590
title: Cotton - Pests and Diseases
DOI: 10.34808/rjk6-ry03
title: Data and codes accompanying the paper: Parteka A., Kordalska A.  "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data".  Technovation, Volume 125, July 2023, 102764

description: The folder contains the data and codes used in the analysis described in the paper:  Parteka A., Kordalska A.  (2023) Artificial intelligence and productivity: global evidence from AI patent and bibliometric data.  Technovation, Volume 125, July 2023, 102764
DOI: 10.48758/ufz.13816
title: Eddy covariance rawdata for agricultural site 'Hordorf' (DE-Hod) in central Germany for DOY091-DOY120, 2023

description: Eddy covariance-Rawdata 2023 Hordorf DoY091-DOY120 (April 2023), acquisistion of CSAT3 and LI7500 in 2.3m height with eddymeas, 20Hz, slt and dia files, meta-information in cfg-files

file: HD2023_Eddy-Data_DOY091-DOY120.zip
more files available in the same repository
DOI: 10.23708/u6wtle
title: Land use land cover high resolution map (10 m) for the area of Tori-Bossito, Benin, 2010-2011

description: This dataset is a high spatial resolution Land Use / Land Cover (LULC) map of the region of Tori-Bossito, southern Benin. Its spatial resolution is 10 m. It was produced in 2011 and made available for a wide range of uses. It has been produced to study the environmental determinants of the presence and abundance of three malaria mosquito species. It contains 14 land cover classes: Freshwater, Herbswamp, Aquatic grassland, Coco tree, Eucalyptus tree, Thicket, Palm tree, Savanna, Teak tree, Pineapple, Degraded riparian forest, Degraded surfaces, Rain-fed agriculture, Forest. The method used to generate the map involved a supervised object-based image classification using mono- and multi-spectral SPOT 5 satellite products from 2010, ground-truth data (&gt; 200 plots) acquired by fieldwork (2010-2011), and nearest-neighbor classifier. The classification accuracy was 98%. In addition to the LULC georeferenced raster data, we propose the following files in this release: the raster attribute table, as an .ods (libreoffice) file, including names and definitions of the land cover classes in both English and French ; a QGIS layer style file (.qml) for visualizing the raster in QGIS ; two color map files (.clr) (English and French) to be used in a variety of GIS software. the map as a .png miniature image ; representative pictures of the land cover classes ; The methodology used to generate the data was detailed in french in Moiroux 2012 (https://theses.hal.science/tel-00812118, p. 79-85 ) and in English, in a more concise way in Moiroux et al. 2013 ( https://doi.org/10.1186/1756-3305-6-71).
DOI: 10.57780/esa-13t59i2
title: S1-L-DCIXS-2-EDR-LP

description: This dataset contains the RAW data of the DCIXS instrument from the Lunar Phase of the SMART-1 mission. The individual data products contain the different types of spectra. The XSM data and HK data is provided as separate data products. [truncated see actual data for full text]
DOI: 10.57780/esa-7fr51bk
title: S1-X-DCIXS-2-EDR-EEP

description: This dataset contains the RAW data of the DCIXS instrument from the Earth Escape Phase of the SMART-1 mission. The individual data products contain the different types of spectra. The XSM data and HK data is provided as separate data products. [truncated see actual data for full text]
DOI: 10.57780/esa-bywwnf7
title: S1-L-DCIXS-2-EDR-EP

description: This dataset contains the RAW data of the DCIXS instrument from the Extended Mission Phase of the SMART-1 mission. The individual data products contain the different types of spectra. The XSM data and HK data is provided as separate data products. [truncated see actual data for full text]
DOI: 10.57780/esa-zxmajkx
title: S1-L-X-SIR-2-EDR-LP

description: This dataset contains the RAW data of the SIR instrument from the LUNAR PHASE of the SMART-1 mission. The principle science data are spectra. All spectra of each orbit are collected in one data product. No processing beyond unpacking has been applied to the telemetry data. [truncated see actual data for full text]
DOI: 10.6084/m9.figshare.22730213
title: Magnetic and geochemical data of the Beigou loess section in the southern Hunshandake Sandy Land, Inner Mongolia

description: Magnetic and geochemical data of the Beigou loess section.
DOI: 10.6084/m9.figshare.22730213.v4
title: Magnetic and geochemical data of the Beigou loess section in the southern Hunshandake Sandy Land, Inner Mongolia

description: Magnetic and geochemical data of the Beigou loess section.
DOI: 10.34740/kaggle/dsv/5823670
title: AutoXGB
DOI: 10.26050/wdcc/pmmxmcrtdip111
title: PalMod2 MPI-M MPI-ESM1-2-CR Transient Simulations of the Last Deglaciation with prescribed ice sheets from ICE-6G reconstructions (r1i1p1f1)

description: Project: PalMod2 - From the last interglacial to the anthropocene - modeling a complete glacial cycle. The PalMod project (https://www.palmod.de/) aims for a better understanding of how slow feedbacks in the Earth system operate. Using the new insights into Earth system dynamics, PalMod will also make projections of the future climate over the next few millennia. The project PalMod is in the second phase and funded by the German Federal Ministry of Education and Research as a Research for Sustainability Initiative. Summary: The transient simulation was performed with the Max Planck Institute for Meteorology Earth System Model version 1.2 in coarse resolution (MPI-ESM-CR). The model includes the spectral atmospheric model ECHAM6.3 at T31 horizontal resolution (approx. 3.75°) and 31 vertical levels, the land surface vegetation model JSBACH3.2, and the primitive equation ocean model MPIOM1.6 with a nominal resolution of 3°. The applied setup was introduced in detail in Kapsch et al. (2021 and 2022). For the experiment, the model was integrated from a glacial state at 26 ka to the year 1950 with prescribed atmospheric greenhouse gas concentrations (Köhler et al., 2017) and insolation (Berger & Loutre, 1991). Ice sheets and surface topographies were prescribed from ICE-6G (Peltier et al., 2015) reconstructions. All forcing fields are updated every 10 years of the simulations and initiate changes in the topography, glacier mask, river pathways, ocean bathymetry, and land-sea mask. Meltwater from ice sheets is calculated as the temporal derivative of ice thickness at grid points covered by grounded ice sheets. The derived meltwater is then distributed by the hydrological discharge model and finally released into the ocean as freshwater. The experiment was performed as part of a model ensemble that contains simulations differing in terms of their tuning parameters, parameterizations and bug fixes. The ensemble is described in detail in the Supplementary Material of Kapsch et al. (2022). The current simulation refers to ICE6G_P1 in Kapsch et al. (2022) and is significantly warmer during the glacial than model versions P2 and P3, indicating a weaker sensitivity to greenhouse gas changes. The glacial cooling over the North Atlantic is more realistic in P2 and P3 than in P1. P2 and P3 are also more sensitive to small changes in the meltwater forcing than P1. The experiment was performed as part of a model ensemble that contains simulations differing in terms of their tuning parameters, parameterizations, bug fixes and forcing. These differences are described according to the CMIP6 nomenclature, where r denotes the realization, i the initialization method, p differences in the physics and f in the forcing. The ensemble is described in detail in the Supplementary Material of Kapsch et al. (2022).
DOI: 10.34740/kaggle/dsv/5823663
title: Cotton - Pest and Disease
DOI: 10.57780/esa-c9qazg7
title: RO-SS-RPCMAG-3-RVM1-CALIBRATED

description: This dataset contains CALIBRATED (CODMAC LEVEL 3) DATA of the RENDEZVOUS MANOEUVRE 1 (RVM1) from November 22 2010 until December 06 2010 of the ROSETTA orbiter magnetometer RPCMAG. The current version of the dataset is V9.0. The difference to the datasets of earlier versions is mainly a significantly improved sensor temperature model more detailed documentation about magnetic disturbance sources more data handling information for the user and also an improved quality flagging system. [truncated see actual data for full text]
DOI: 10.34740/kaggle/dsv/5823676
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.57780/esa-0bxs99c
title: MEX-M-HRSC-4-REFDR-MAPPROJECT-EXT7

description: This CODMAC level 4 data set contains geo referenced and map projected data products acquired by the HRSC camera on the Mars Express spacecraft covering the orbits 18976 to 21487. [truncated see actual data for full text]
DOI: 10.57780/esa-0kgjn1s
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT1

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the first mission extension (January 1 2006 - September 30 2007). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-4fch0w5
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT4

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the fourth mission extension (January 1 2013 - Dec. 31 2014). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-b0hg1v1
title: MEX-M-HRSC-4-REFDR-MAPPROJECT-EXT8

description: This CODMAC level 4 data set contains geo referenced and map projected data products acquired by the HRSC camera on the Mars Express spacecraft covering the orbits 21488 to 23996. [truncated see actual data for full text]
DOI: 10.57780/esa-io8dcmi
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT5

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the fifth mission extension (January 1 2015 - Dec. 31 2016). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-ln68inw
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT2

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the second mission extension (October 1 2007 - May 31 2009). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-mipym7u
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT7

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the sixth mission extension (January 1 2017 - Dec. 31 2018). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-o1b4o6o
title: MEX-M-ASPERA3-2_3-EDR_RDR-NPI-EXT6

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the sixth mission extension (January 1 2017 - Dec. 31 2018). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.57780/esa-xw2hczu
title: MEX-M-ASPERA3-2-3-EDR-RDR-NPI-EXT3

description: This data set contains Mars Express ASPERA-3 Neutral Particle Imager (NPI) data for the third mission extension (January 1 2010 - Dec. 31 2012). These data are provided in units of count rate (counts/second). [truncated see actual data for full text]
DOI: 10.5281/zenodo.7447490
title: Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1

description: <strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
description: {"references": ["Mothes, R. and Pascual-Reguant, A. et al. Local CCL18 and CCL21 expand lung fibrovascular niches and recruit lymphocytes, leading to tertiary lymphoid structure formation in prolonged COVID-19. medRxiv 2022.03.24.22272768 (2022) doi:10.1101/2022.03.24.22272768."]}
DOI: 10.5281/zenodo.7447491
title: Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1

description: <strong>Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1)</strong> Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
description: {"references": ["Mothes, R. and Pascual-Reguant, A. et al. Local CCL18 and CCL21 expand lung fibrovascular niches and recruit lymphocytes, leading to tertiary lymphoid structure formation in prolonged COVID-19. medRxiv 2022.03.24.22272768 (2022) doi:10.1101/2022.03.24.22272768."]}
DOI: 10.5281/zenodo.7015306
title: CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading

description: Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files. Examples: S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel Z-Stack <strong>was not</strong> activated inside acquisition experiment S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels Z-Stack <strong>was </strong>activated inside acquisition experiment The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.
DOI: 10.5281/zenodo.7015307
title: CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading

description: Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files. Examples: S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel Z-Stack <strong>was not</strong> activated inside acquisition experiment S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels Z-Stack <strong>was </strong>activated inside acquisition experiment The test files (so far) contain not any data with more "advanced" dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.
DOI: 10.5281/zenodo.6645977
title: 3D Nuclei annotations and StarDist 3D model(s) (rat brain)

description: <strong>Name</strong>: 3D Nuclei annotations and StarDist3D model(s) (rat brain) <strong><em>Images: </em></strong>From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/) These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62). <strong><em>Conda environment</em></strong><em>: </em>A conda environment was created using the yml file <em>stardist0.8_TF1.15.yml</em> <strong><em>Training : </em></strong>Training was performed using the jupyter notebook <em>1-Training_notebook.ipynb</em>.<br> Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/) Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/). <strong><em>Validation: </em></strong>To save metrics in a csv file and compare predictions to the annotations the jupyter notebook <em>2-QC_notebook.ipynb </em>can be used on the validation folder. <strong>Large images</strong>: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif )<br> These images were obtained using the plugin BigSticher on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.
DOI: 10.5281/zenodo.6645978
title: 3D Nuclei annotations and StarDist 3D model(s) (rat brain)

description: <strong>Name</strong>: 3D Nuclei annotations and StarDist3D model(s) (rat brain) <strong><em>Images: </em></strong>From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/) These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62). <strong><em>Conda environment</em></strong><em>: </em>A conda environment was created using the yml file <em>stardist0.8_TF1.15.yml</em> <strong><em>Training : </em></strong>Training was performed using the jupyter notebook <em>1-Training_notebook.ipynb</em>.<br> Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/) Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/). <strong><em>Validation: </em></strong>To save metrics in a csv file and compare predictions to the annotations the jupyter notebook <em>2-QC_notebook.ipynb </em>can be used on the validation folder. <strong>Large images</strong>: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif )<br> These images were obtained using the plugin BigSticher on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.
DOI: 10.5281/zenodo.6523648
title: Liver Micrometastases area quantification using QuPath and pixel classifier

description: <strong>Sample</strong>: Mouse (NSG) liver slices with human colorectal cancer cells metastases, stained with Hematoxylin & Eosin. <strong>Image Acquisition</strong>: Images were acquired on an Olympus VS120 Whole Slide Scanner, using a 20x objective (UPLSAPO, N.A. 0.75) and a color camera (Pike F505 Color) with an image pixel size of 0.345 microns. <strong>Image Processing and Analysis</strong>: Obtained images were analyzed using the software QuPath [1] (version 0.3.2) using groovy scripts, making use of a pixel classifier to segment and measure cancer cell clusters. <strong>Files</strong> : <em>Detailed_worflow.pdf</em> : contains a detailed description of how pixel classifier was created <em>images_for_classifier_training.zip</em> : contains all the vsi file obtained from the microscope and used for the training <em>project_for_classifier_training.zip</em> : contains the QuPath project, with Training Image, annotations, classifiers and scripts for analysis <em>PythonCode.txt</em> : code ran to transform output results from QuPath to final results [1] Bankhead, P. et al. <strong>QuPath: Open source software for digital pathology image analysis</strong>. <em>Scientific Reports</em> (2017). https://doi.org/10.1038/s41598-017-17204-5