cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20210537
title: Supplementary Material for: KARYOTYPING OF LYMPHOCYTES AND EPITHELIAL CELLS OF DISTINCT EMBRYONIC ORIGIN DOESN’T HELP TO PREDICT THE TURNER SYNDROME FEATURES

description: Background: In Turner syndrome (TS), fluorescent in situ hybridization (FISH) karyotyping offers an alternative to classical karyotyping. Objective: We tested the added value of FISH karyotyping from lymphocytes (mesodermal origin), buccal cells (ectodermal origin) and a rear-tongue smear (endodermal origin) to determine the 45,X cell line fraction and its impact on patient phenotype. Design and Patients: Classical karyotyping and three FISH assays were done in 153 girls and women previously diagnosed with TS in four university hospitals. The 45,X cell line fraction was determined for each method and correlated with the major phenotypic signs. Results: Classical karyotyping identified 45,X/46,XX mosaicism in 77/153 subjects (50%), 45,X monosomy in 52/153 (34%), and other karyotypes in 24/153 (16%). FISH from lymphocytes verified 45,X in 47/52 original cases, whereas 4/52 had 45,X/46,XX and 1/52 45,X/47,XYY mosaicism. The 45,X cell line fraction was higher in FISH from lymphocytes compared to classical karyotyping (median 86.4% versus 70.0%; p&lt;0.001), while there was no difference for FISH from buccal or rear-tongue smear cells. The mean 45,X cell line fraction was more abundant in patients with several of the characteristic phenotypic signs compared to patients without them (p&lt;0.01), but the predictive power was insufficient. Conclusion: FISH analysis confirmed the findings of classical karyotyping; only a few 45,X monosomy cases were reclassified as mosaics. The 45,X cell line fraction did not show clinically meaningful prediction of the phenotype. FISH analysis of buccal or rear-tongue epithelial cells may be a non-inferior, less invasive alternative to classical karyotyping.
DOI: 10.6084/m9.figshare.14484480
title: RC-CluSPTP instances

description: Dataset for the Resource Constrained Clustered Shortest Path Tree Problem.<br>
DOI: 10.6084/m9.figshare.14484480.v1
title: RC-CluSPTP instances

description: Dataset for the Resource Constrained Clustered Shortest Path Tree Problem.<br>
DOI: 10.6084/m9.figshare.20710069
title: In vitro, in silico antimicrobial and antioxidative profiles of novel thiourea derivatives of 4-nitrophenyl isothiocyanate: Biocomputational approach

description: Manuscript along with supporting information
DOI: 10.6084/m9.figshare.20710069.v1
title: In vitro, in silico antimicrobial and antioxidative profiles of novel thiourea derivatives of 4-nitrophenyl isothiocyanate: Biocomputational approach

description: Manuscript along with supporting information
DOI: 10.5281/zenodo.7030916
title: Dataset of MSCA_IF_CF_CO2R_838686

description: This dataset includes data generated along this MSCA program.
DOI: 10.5281/zenodo.7030915
title: Dataset of MSCA_IF_CF_CO2R_838686

description: This dataset includes data generated along this MSCA program.
DOI: 10.5281/zenodo.7030958
title: Sea surface Dimethylsulfide Concentration and Emission Flux over the North Atlantic Ocean

description: The data represent the monthly climatology of sea surface Dimythylsulfide (DMS) concentration and sea-to-air Dimythylsulfide flux (FDMS) over the North Atlantic Ocean at 0.25°×0.25° spatial resolution. DMS data were obtained by applying a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998-2021). FDMS derived from the predicted DMS concentrations (GPR) and Goddijn-Murphy et al. (2012) parametrization. The scripts are authored by K. Mansour. For more information, please contact me at k.mansour@isac.cnr.it.
DOI: 10.5281/zenodo.7030957
title: Sea surface Dimethylsulfide Concentration and Emission Flux over the North Atlantic Ocean

description: The data represent the monthly climatology of sea surface Dimythylsulfide (DMS) concentration and sea-to-air Dimythylsulfide flux (FDMS) over the North Atlantic Ocean at 0.25°×0.25° spatial resolution. DMS data were obtained by applying a machine learning predictive algorithm based on Gaussian process regression (GPR) to model the distribution of daily DMS concentrations in the North Atlantic waters over 24 years (1998-2021). FDMS derived from the predicted DMS concentrations (GPR) and Goddijn-Murphy et al. (2012) parametrization. The scripts are authored by K. Mansour. For more information, please contact me at k.mansour@isac.cnr.it.
DOI: 10.5281/zenodo.7018859
title: Temperature-dependent Lamb wave signals in highly anisotropic CFRP

description: The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data. The following parameters were used during measurements: Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50]; Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3]; Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250]; Number of averages: 50; Sampling frequency: 10 MHz. The following equipment was used in the experiment: Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C; National Instruments waveform generator PXIe-5413; Krohn-Hite voltage amplifier model 7500; Cedrat Technologies LWDS amplifier (used as a charge amplifier); National Instruments oscilloscope PXIe-5105. Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.
description: {"references": ["[1] P. Kudela, M. Radzie\u0144ski, P. Fiborek, T. Wandowski, Elastic constants identification of fibre-reinforced composites by using guided wave dispersion curves and genetic algorithm for improved simulations, Composite Structures, 272, 2021, 114178"]}
DOI: 10.5281/zenodo.7018858
title: Temperature-dependent Lamb wave signals in highly anisotropic CFRP

description: The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data. The following parameters were used during measurements: Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50]; Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3]; Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250]; Number of averages: 50; Sampling frequency: 10 MHz. The following equipment was used in the experiment: Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C; National Instruments waveform generator PXIe-5413; Krohn-Hite voltage amplifier model 7500; Cedrat Technologies LWDS amplifier (used as a charge amplifier); National Instruments oscilloscope PXIe-5105. Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.
description: {"references": ["[1] P. Kudela, M. Radzie\u0144ski, P. Fiborek, T. Wandowski, Elastic constants identification of fibre-reinforced composites by using guided wave dispersion curves and genetic algorithm for improved simulations, Composite Structures, 272, 2021, 114178"]}
DOI: 10.25442/hku.20552013
title: supporting data for "The role of endometrial gland in regulating trophoblast functions and its association with the pathogenesis of pre-eclampsia."

description: This is the raw data for " The role of endometrial gland in regulating trophoblast functions and its association with the pathogenesis of pre-eclampsia. " After the organoid successful established, we did the Omics study, and then validated the target gene/miRNA/protein relationships. At last, a series biological functinal experment were used to detect the potential factors functions.
DOI: 10.25442/hku.20552013.v1
title: supporting data for "The role of endometrial gland in regulating trophoblast functions and its association with the pathogenesis of pre-eclampsia."

description: This is the raw data for " The role of endometrial gland in regulating trophoblast functions and its association with the pathogenesis of pre-eclampsia. " After the organoid successful established, we did the Omics study, and then validated the target gene/miRNA/protein relationships. At last, a series biological functinal experment were used to detect the potential factors functions.
DOI: 10.5281/zenodo.7030892
title: Data for publication "Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering"

description: - plotting scripts for the RIXS spectra presented in the paper - analysis scripts for circular dichroism as function of the electric field - all spectra in HDF5 format
DOI: 10.5281/zenodo.7030891
title: Data for publication "Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering"

description: - plotting scripts for the RIXS spectra presented in the paper - analysis scripts for circular dichroism as function of the electric field - all spectra in HDF5 format
DOI: 10.5281/zenodo.7025143
title: Acceleration Research on Novel Photovoltaic Materials

description: Data and Simulation definiton file for SCAPS1D simulation that are the basis for figures 3-5 of publication DOI:10.1039/d2fd00085g, published in Faraday Discussions (2022) Device structure for the drift-diffusion simulation is: metal back-contact/p-type absorber(1 micron)/n-type buffer layer (30nm)/i-ZnO(80nm)/n-type ZnO(100nm) No interface recombination and no back contact recombination is assumed.
DOI: 10.5281/zenodo.7025144
title: Acceleration Research on Novel Photovoltaic Materials

description: Data and Simulation definiton file for SCAPS1D simulation that are the basis for figures 3-5 of publication DOI:10.1039/d2fd00085g, published in Faraday Discussions (2022) Device structure for the drift-diffusion simulation is: metal back-contact/p-type absorber(1 micron)/n-type buffer layer (30nm)/i-ZnO(80nm)/n-type ZnO(100nm) No interface recombination and no back contact recombination is assumed.
DOI: 10.5281/zenodo.7030982
title: Acceleration Research on Novel Photovoltaic Materials

description: Data and Simulation definiton file for SCAPS1D simulation that are the basis for figures 3-5 of publication DOI:10.1039/d2fd00085g, published in Faraday Discussions (2022) Device structure for the drift-diffusion simulation is: metal back-contact/p-type absorber(1 micron)/n-type buffer layer (30nm)/i-ZnO(80nm)/n-type ZnO(100nm) No interface recombination and no back contact recombination is assumed.
DOI: 10.5281/zenodo.5591878
title: Nomad-FAIR North-Remote-Tool Example Dataset Electron Microscopy

description: <strong>Dataset.Objective.Aperture.Energy.Slit.cdf.nx5.zip<br> Dataset.Objective.Aperture.Energy.Slit.dm3.cdf.zip<br> parser-focalseries-gatandm-cdf2nx5.py</strong><br> An example dataset for focus-series reconstruction (in-line holography).<br> The dataset supplements/comes from a research study https://doi.org/10.1016/j.ultramic.2021.113264 <strong>em-sprint09-example.zip</strong> This archive contains an example YAML file how metadata to e.g. 1613_Si_HAADF_610_kx.emd<br> (see below) are stored internally in a NOMAD OASIS when saving entries into the customized<br> ELN schema of the EM example that was developed in Sprint09 Area B in the FAIRmat project.<br> This eln_data.yaml file serves as an input to the dataconverter/em reader tool to supplement<br> the numerical data from the 1613_Si_HAADF_610_kx.emd file with metadata during the creation<br> of the emd.nxs file. Furtermore, this archive contains a Bruker BCF, a Velox EMD, and a Digital Micrograph v3 DM3<br> file. These file formats can be used as input to the dataconverter/em reader tool to inject<br> specific numerical data while creating an instance of an NXem-compliant NeXus file. The files have the following SHA256 checksum to verify their integriety: sha256sum eln_data.yaml<br> c0fd230336ce7cd8e2715445ea6af404202b4ab9d7e7186838748bd0559d536f sha256sum 46_ES-LP_L1_brg.bcf<br> bf5ca0f862af49726b848a8a60d73af6f1669990131288ac4b4ca6cb57e58aff sha256sum 1613_Si_HAADF_610_kx.emd<br> 6ed0e8b8c170a0ed307e5c877fd891767a38e636f4680632e9b00c46a2b963b9 sha256sum EELS_map_2_ROI_1_location_4.dm3<br> b97850f6c6b100740813c34d20eda294ef66a6130daa42a6cf975c4572ff599c
DOI: 10.5281/zenodo.5591879
title: Nomad-FAIR North-Remote-Tool Example Dataset Electron Microscopy

description: An example dataset for focus-series reconstruction (in-line holography).<br> The dataset supplements/comes from a research study https://doi.org/10.1016/j.ultramic.2021.113264
DOI: 10.5281/zenodo.7030811
title: Nomad-FAIR North-Remote-Tool Example Dataset Electron Microscopy

description: <strong>Dataset.Objective.Aperture.Energy.Slit.cdf.nx5.zip<br> Dataset.Objective.Aperture.Energy.Slit.dm3.cdf.zip<br> parser-focalseries-gatandm-cdf2nx5.py</strong><br> An example dataset for focus-series reconstruction (in-line holography).<br> The dataset supplements/comes from a research study https://doi.org/10.1016/j.ultramic.2021.113264 <strong>em-sprint09-example.zip</strong> This archive contains an example YAML file how metadata to e.g. 1613_Si_HAADF_610_kx.emd<br> (see below) are stored internally in a NOMAD OASIS when saving entries into the customized<br> ELN schema of the EM example that was developed in Sprint09 Area B in the FAIRmat project.<br> This eln_data.yaml file serves as an input to the dataconverter/em reader tool to supplement<br> the numerical data from the 1613_Si_HAADF_610_kx.emd file with metadata during the creation<br> of the emd.nxs file. Furtermore, this archive contains a Bruker BCF, a Velox EMD, and a Digital Micrograph v3 DM3<br> file. These file formats can be used as input to the dataconverter/em reader tool to inject<br> specific numerical data while creating an instance of an NXem-compliant NeXus file. The files have the following SHA256 checksum to verify their integriety: sha256sum eln_data.yaml<br> c0fd230336ce7cd8e2715445ea6af404202b4ab9d7e7186838748bd0559d536f sha256sum 46_ES-LP_L1_brg.bcf<br> bf5ca0f862af49726b848a8a60d73af6f1669990131288ac4b4ca6cb57e58aff sha256sum 1613_Si_HAADF_610_kx.emd<br> 6ed0e8b8c170a0ed307e5c877fd891767a38e636f4680632e9b00c46a2b963b9 sha256sum EELS_map_2_ROI_1_location_4.dm3<br> b97850f6c6b100740813c34d20eda294ef66a6130daa42a6cf975c4572ff599c
DOI: 10.15454/xwb6rl/uci7b1
title: m3d_dump.rar

description: :unav
DOI: 10.6084/m9.figshare.20479515.v1
title: Water Hyacinth Samples in Lake Tana.xlsx

description: The data is about the morphometric and biomass characteristics of water hyacinth on Lake Tana, Ethiopia
DOI: 10.15454/yrhl93/g0065n
title: tables_code.sql

description: :unav
DOI: 10.15454/yrhl93/hoflgd
title: protOLAP.jar

description: :unav
DOI: 10.15454/yrhl93/razvbi
title: Mondrian_Schema_Star.xml

description: :unav
DOI: 10.15454/yrhl93/72v6ta
title: inrae.jpg

description: :unav
DOI: 10.15454/yrhl93/9zgf7j
title: logo.png

description: :unav
DOI: 10.15454/yrhl93/brp60g
title: protOLAPexample.xml

description: :unav
DOI: 10.15454/yrhl93/dfpf4x
title: logoProtOLAP.png

description: :unav
DOI: 10.15454/yrhl93/yugs8u
title: Readme.txt

description: :unav
DOI: 10.5281/zenodo.5647834
title: Porting DB12 to Python3: Analysis of the scores

description: DB12 has been originally conceived with Python2 to estimate the power of a given CPU to run HEP applications. However, since January 2020, Python2 is no longer maintained and we decided to port the code to Python3, which contains several optimizations. In October 2021, we effectively ported DB12 to Python3.9, but the optimizations brought by the language generated discrepancies in the norm score, which is a critical component to evaluate the power of CPUs. We build an analysis tool to mitigate these discrepancies. <strong>Current situation</strong> The current analysis assess the impact of the changes on the norm score and propose 3 different solutions to resolve the issue: Find a constant value that would transform a python3 score into a python2 one: 1.18 seems fine. Pros: simple Cons: not accurate. does fit well with scores computed on Intel, not so well with scores computed on AMD Find one constant value per processor type (Intel/AMD): 1.16 fits well with scores computed on Intel, 1.4 also fits well with scores computed on AMD . Pros: accurate Cons: need to maintain a table and update it with new types of processors, and maybe also with new versions of python Compute a simple linear regression: Pros: accurate Cons: need to run it with many examples to get an accurate model To keep it simple and accurate, we chose to apply the second solution: one constant value per processor type.<br> These constants are part of the code and are located in <em>src/db12/factors.json</em>. These values will need to be updated through time, according to the evolution of the CPUs and Python.<br> If you need to get an accurate DB12 norm score using a Python version or a CPU that has not be taken into account,<br> then you have to run the analysis with new data following the next steps. <strong>Run the analysis</strong> Execute the Jupyter Notebook: <pre><code class="language-bash">jupyter notebook DB12Analysis.ipynb</code></pre> <strong>Include new data using DIRAC</strong> Install a DIRAC client: <pre><code class="language-bash">lb-dirac lhcb-proxy-init</code></pre> Go to <em>resources/tools</em> and submit jobs: <pre><code class="language-bash">./submit.sh &lt;number of jobs&gt; &lt;list of sites&gt;</code></pre> Once the jobs are done, add their IDs in `jobIDs.csv` to get the results: <pre><code class="language-bash">python getDB12Scores.py</code></pre> You will obtain `results.json`, you can sort the file to get a better look at it with `jq`: <pre><code class="language-bash">cat results.json | jq . &gt; results_sorted_&lt;date&gt;.json</code></pre> You can finally remove the jobs that are still pending in the queues
DOI: 10.5281/zenodo.7031029
title: Porting DB12 to Python3: Analysis of the scores

description: DB12 has been originally conceived with Python2 to estimate the power of a given CPU to run HEP applications. However, since January 2020, Python2 is no longer maintained and we decided to port the code to Python3, which contains several optimizations. In October 2021, we effectively ported DB12 to Python3.9, but the optimizations brought by the language generated discrepancies in the norm score, which is a critical component to evaluate the power of CPUs. We build an analysis tool to mitigate these discrepancies. <strong>Current situation</strong> The current analysis assess the impact of the changes on the norm score and propose 3 different solutions to resolve the issue: Find a constant value that would transform a python3 score into a python2 one: 1.18 seems fine. Pros: simple Cons: not accurate. does fit well with scores computed on Intel, not so well with scores computed on AMD Find one constant value per processor type (Intel/AMD): 1.16 fits well with scores computed on Intel, 1.4 also fits well with scores computed on AMD . Pros: accurate Cons: need to maintain a table and update it with new types of processors, and maybe also with new versions of python Compute a simple linear regression: Pros: accurate Cons: need to run it with many examples to get an accurate model To keep it simple and accurate, we chose to apply the second solution: one constant value per processor type.<br> These constants are part of the code and are located in <em>src/db12/factors.json</em>. These values will need to be updated through time, according to the evolution of the CPUs and Python.<br> If you need to get an accurate DB12 norm score using a Python version or a CPU that has not be taken into account,<br> then you have to run the analysis with new data following the next steps. <strong>Run the analysis</strong> Execute the Jupyter Notebook: <pre><code class="language-bash">jupyter notebook DB12Analysis.ipynb</code></pre> <strong>Include new data using DIRAC</strong> Install a DIRAC client: <pre><code class="language-bash">lb-dirac lhcb-proxy-init</code></pre> Go to <em>resources/tools</em> and submit jobs: <pre><code class="language-bash">./submit.sh &lt;number of jobs&gt; &lt;list of sites&gt;</code></pre> Once the jobs are done, add their IDs in `jobIDs.csv` to get the results: <pre><code class="language-bash">python getDB12Scores.py</code></pre> You will obtain `results.json`, you can sort the file to get a better look at it with `jq`: <pre><code class="language-bash">cat results.json | jq . &gt; results_sorted_&lt;date&gt;.json</code></pre> You can finally remove the jobs that are still pending in the queues