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Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 02 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 01 and day 2 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 02 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 00 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 0 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 12 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 12 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 10 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 10 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 14 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 14 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 02 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 04 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 02 and day 04 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 02 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 03 and day 02 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 00 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 03 and day 00 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 06 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 03 and day 06 after drought stress application.

Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 04 - raw image data

Cowpea (Vigna unguiculata) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected.  This dataset represents the images collected for Screen number 03 and day 04 after drought stress application.

Harmonized analysis of microplastics: Insights from practical application within the FACTS project

The pollution of the environment with plastics is of emerging concern. While first initiatives aim to reduce the input of these materials, the already existing amounts pose a problem. Due to degradation and fragmentation the initially large items form micro- and nanoplastic particles. Yet the analysis of these contaminates is hampered due to increasing number of analytical pipelines and tools in the different laboratories. In the recent years, various projects aimed to provide information about these issues at different scales. Here, we present the harmonization procedures derived and applied within the "Fluxes and Fate of Microplastics in Northern European Waters" (FACTS) project. To achieve a project internal harmonization the available sampling techniques, sample extraction methods, quality assurance measures aand protocols as well as analytical techniques were collected, evaluated and finally transferred into commonly agreed harmonized procedures. These procedures were afterwards applied in the planning of a large scale sampling campaign along the Norwegian Coast and within the Bergen Fjord. This was achieved by defining the filter mesh sizes for filtration pumps and sampling nets as well as the amount of sediment sampled. In contrast to sampling, the extraction was more difficult to harmonize yet a common agreement was derived by using Fenton's reagent oxidation for the removal of organic matter and sodium bromide for density separation following the laboratory internal protocol. Finally, the for analysis FTIR microscopy shall be applied using a common database and software tool (siMPle) allowing a following pyrolysis gas chromatography-mass spectrometry (py-GC/MS) analysis. The experience during the application of these protocols within FACTS will be discussed and further contextualized with the results of the "EUROpean Quality Controlled Harmonisation Assuring Reproducible Monitoring and assessment of plastic pollution" (EUROqCHARM) project. Also see: https://micro2022.sciencesconf.org/427364/document

A Collection of Papers on Blockchain Business Applications

We performed a systematic literature review of 63 research papers reporting blockchain business applications,  published between 2016 and 2020. These papers were selected through snowballing. There are 53 journal papers, 8 conference papers, and 2 book chapters. From these papers, we identified approximately 408 blockchain applications through qualitative  content analysis. We classified these applications into three categories or types: IoT-Based, Enterprise-Centric and Consumer-Centric.

Perceptions and Knowledge regarding the application of Artificial Intelligence (AI) technologies in healthcare system; A cross-sectional Study involving Medical Students.

This is the data set of a cross-sectional study that was carried out on medical students at Rawalpindi medical university, Pakistan involving 211 participants.

RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area

This dataset contains all Radiative Transfer (RT) results made for the paper. The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations: <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt). In these folders, each VIMS pixel is stored in a .txt file with the following pattern: <CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt It starts with a header describing the observation:  <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs:  <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> Then contains the observed spectra: <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for:  <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km): <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>

opencc-by-4.0Nov 2022View details →

Dataset and Application of Algorithms of "Towards a More Set of Acceptance Criteria"

This dataset contains the requirements of the Corona Warn App together with the results of the application of the algorithms described in "Towards a More Complete Set of Acceptance Criteria Using User Story Similarity" on this data set.

opencc-by-4.0Feb 2023View details →

A microwave link simulation dataset for rainfall observation applications

A microwave link simulation dataset is developed for theoretical research of rainfall observation applications. The dataset is built based on electromagnetic propagation theory attenuation effects with measured raindrop size distribution data from a PARSIVEL disdrometer and air temperature, air pressure and humidity data from nearby weather stations. Frequencies from 6 to 180 GHz, horizontal or vertical polarization, link lengths from 0.1 km to 20 km, and quantization resolutions from 0 to 1 dB are all fully considered for various existing and future complications. The dataset can be utilised to validate the performance of the microwave link-based rainfall inversion process algorithm in various equipment configurations and to assess the potential of microwave link-based rainfall monitoring technology in future high-frequency communication infrastructure conditions on a theoretical level with a very low cost.

opencc-by-4.0Feb 2023View details →

supplementary to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" , 2D wind forcing

This is a supplementary material to article "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" ( unpublished at the publication date of this dataset) It contains the 2D wind forcing for Crocus-SnowPappus simulations on the Grandes Rousses test zone. It was generated using DEVINE wind downscaling method ( Le Toumelin et al., 2022 )