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Data of Fig6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig6.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_1-3.csv).</p> <p> </p>
Data of Fig3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig3.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M_1.pdf).</p>
Data of Fig1, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig1, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (doi: 10.3390/cancers14133074) contains the original figures as PNG-format (10.3390-cancers14133074_Fig1.PNG). Raw data and related Meta data are provides as one file in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M.txt) and one file in PDF-Format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M_1.pdf).</p>
Data of Fig9, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig9, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig9.PNG). The Corresponding raw data and subsequent data analysis obtained for immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M.pdf) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt)</p> <p> </p>
Data of FigS3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS3.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3_M .txt), six files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-1-6 .csv) and one file in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-4 .sps). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M 1.pdf).</p>
Data of FigS5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS5, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M.txt).</p> <p> </p>
Data of FigS6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS6.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains two files in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1-2_M.txt). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf).</p> <p> </p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
Natrolite - Sample 2 NanED Round Robin, Data: ESR4 & ESR5
<p><em><strong>Natrolite</strong></em></p> <p>The following submission contains the data collection and processing of datasets for the sample natrolite under the NanEd round-robin project. A single crystal was identified, and continuous rotation and precession data acquisition techniques were used to collect datasets on the same crystal. All the data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder.</p> <p><strong>Continuous rotation</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR4 & ESR5 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR1-S2_PRAHA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR1-S2_PRAHA-CROT</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td>Transmission electron microscope FEI Tecnai G2 20</td> </tr> <tr> <td> <p>Radiation source</p> </td> <td>LaB6</td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td>200 kV</td> </tr> <tr> <td> <p>Wavelength</p> </td> <td>0.0251 Å</td> </tr> <tr> <td> <p>Probe Type</p> </td> <td>Microdiffraction</td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td>900nm</td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td>Parallel beam, convergence <0.1mrad</td> </tr> <tr> <td> <p>Detector</p> </td> <td>Hybrid pixel detector ASI Cheetah (side mounted)</td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td>512 x 512</td> </tr> <tr> <td> <p>Pixel size</p> </td> <td>55 µm x 55 µm</td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td>1000 mm</td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td>0.008259 Å<sup>-1</sup>/pixel</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td><strong>Sample description:</strong></td> <td> </td> </tr> <tr> <td> <p>Name</p> </td> <td>Natrolite</td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>10</sub>.2H<sub>2</sub>O</td> </tr> <tr> <td> <p>Sample source</p> </td> <td>Natural sample from Marianska Skala, Usti nad Labem, Czechia</td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td>Powder crushed in an agate mortar and deposited on a Cu grid with holey C film</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td>95 K</td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td>1</td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>240</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-60° to +60°, 0.5°, 0.5°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>482ms</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Software: </strong></p> </td> <td> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td>Software used for processing </td> <td> <p>PETS2 (ver 2.2.20220701.0941)</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Hrushikesh Chintakindi (ESR4) & Ashwin Suresh (ESR5)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> </td> </tr> <tr> <td>Image folder</td> <td> <p>dpCROT : Folder containing images of the diffraction pattern from each frame.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td>Crystal_image.jpg : image of the crystal</td> </tr> <tr> <td> <p> </p> </td> <td>S2_C5-crot-050_petsdata : Log files of PETS2 processing</td> </tr> <tr> <td> <p> </p> </td> <td>S2_C5-crot-050.pts2:_input file for the program PETS2 used for processing the data</td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Notes: </p> <p>The software, RATS used for data collection is an in-house software developed in FZU, Praha.</p> <p>*Project Label "RR1" stands for Round Robin 1</p> <p>*Data set label "RR1-S2_PRAHA-CROT" stands for Round Robin 1 sample 2 from Praha, data collection method Continuous Rotation.</p> <p>*The additional files can be found in the folder:</p> <p>Continuous_rotation\Data_collection_processing</p> </td> </tr> </tbody> </table> <p><strong>Precession</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR4 & ESR5 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR1-S2_PRAHA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR1-S2_PRAHA-PREC</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope FEI Tecnai G2 20</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Microdiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>900nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Cheetah (side mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>1000 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.008259 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Natrolite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Na<sub>2</sub>Al<sub>2</sub>Si<sub>3</sub>O<sub>10</sub>.2H<sub>2</sub>O</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Natural sample from Marianska Skala, Usti nad Labem, Czechia</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu grid with holey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>95 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>121</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-60° to +60°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>500ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 (ver 2.2.20220701.0941)</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Hrushikesh Chintakindi (ESR4) & Ashwin Suresh (ESR5)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>dp100 : Folder containing images of the diffraction pattern from each frames.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal_image : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S2_C5_prec-100_petsdata : Log files of PETS2 processing</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S2_C5_prec-100.pts2 :_input file for the program PETS2 used for processing the data</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>The software, RATS used for data collection is an in-house software developed in FZU, Praha.</p> <p>*Project Label "RR1" stands for Round Robin 1</p> <p>*Data set label "RR1-S2_PRAHA-PREC" stands for Round Robin 1 sample 2 from Praha, data collection method Precession.</p> <p>*The additional files can be found in the folder:</p> <p>Precession\Data_collection_processing</p> </td> </tr> </tbody> </table> <p> </p>
Epidote - Sample 1 NanED Round Robin, Data: ESR4 & ESR5
<p><em><strong>Epidote</strong></em></p> <p>The following submission contains the data collection and processing of datasets for the sample epidote under the NanEd round-robin project. A single crystal was identified, and continuous rotation and precession data acquisition techniques were used to collect datasets on the same crystal. All the data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder.</p> <p><strong>Continuous Rotation:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR4 & ESR5 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR1-S1_PISA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR1-S1_PISA-CROT</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope FEI Tecnai G2 20</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Microdiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>900nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Cheetah (side mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>1200 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.006895 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Epidote</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Ca<sub>2</sub>Fe<sub>x</sub>Al<sub>3-x</sub>Si<sub>3</sub>O<sub>13</sub>H</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Natural source from Val d'Ossola, Italy</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu grid with holey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>240</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-60° to +60°, 0.5°, 0.5°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>709ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 (ver 2.2.20220701.0941)</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Hrushikesh Chintakindi (ESR4) & Ashwin Suresh (ESR5)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>dpCROT : Folder containing images of the diffraction pattern from each frames.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal_image : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S1_04c-crot-050_petsdata : Log files of PETS2 processing</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S1_04c-crot-050.pts2 :_input file for the program PETS2 used for processing the data</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>The software, RATS used for data collection is an in-house software developed in FZU, Praha.</p> <p>*Project Label "RR1" stands for Round Robin 1</p> <p>*Data set label "RR1-S1_PISA-CROT" stands for Round Robin 1 sample 1 from Pisa, data collection method Continuous Rotation.</p> <p>*The additional files can be found in the folder:</p> <p>Continuous_rotation\Data_collection_processing</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Precession:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR4 & ESR5 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR1-S1_PISA</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR1-S1_PISA-PREC</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope FEI Tecnai G2 20</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Microdiffraction</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>900nm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Cheetah (side mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>1200 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.006895 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Epidote</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Ca<sub>2</sub>Fe<sub>x</sub>Al<sub>3-x</sub>Si<sub>3</sub>O<sub>13</sub>H</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Natural source from Val d'Ossola, Italy</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu grid with holey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Precession</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>121</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>-60° to +60°, 1°, 0°</p> </td> </tr> <tr> <td> <p>Precession angle</p> </td> <td> <p>1°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>700ms</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>RATS software</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2 (ver 2.2.20220701.0941)</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Hrushikesh Chintakindi (ESR4) & Ashwin Suresh (ESR5)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>dp100 : Folder containing images of the diffraction pattern from each frames.</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>tiff_16bit</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal_image : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S1_04p-100_petsdata : Log files of PETS2 processing</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>S1_04p-100.pts2 : input file for the program PETS2 used for processing the data</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>The software, RATS used for data collection is an in-house software developed in FZU, Praha.</p> <p>*Project Label "RR1" stands for Round Robin 1</p> <p>*Data set label "RR1-S1_PISA-PREC" stands for Round Robin 1 sample 1 from Pisa, data collection method Precession.</p> <p>*The additional files can be found in the folder:</p> <p>Precession\Data_collection_processing</p> </td> </tr> </tbody> </table>
Bayesian Samples and Data Behind Figures: Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I
<p>Auxiliary data associated with the article <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.6145P/abstract">"Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I: M 35, NGC 6819, NGC 188" by Penev, K & Schussler, J</a></p> <p>The type of data corresponds to a particular filename format. Bayesian samples are in HDF5 format, directly as saved by the <a href="https://emcee.readthedocs.io/en/stable/index.html">emcee</a> sampler (see <a href="https://emcee.readthedocs.io/en/stable/user/backends/">https://emcee.readthedocs.io/en/stable/user/backends/</a>). All other files are in AAS-journal style machine readable tables format generated by <a href="https://github.com/cds-astro/cds.pyreadme">cdspyreadme</a> python library.</p> <p>Description of contents by filename format:</p> <pre><code><CLUSTER>_<BINARY ID>_.*.h5</code></pre> <p>Bayesian analysis samples constraining the tidal dissipation efficiency of the given binary. The values of the sampled system and tidal dissipation parameters are stored as blobs (<a href="https://emcee.readthedocs.io/en/stable/user/blobs/">https://emcee.readthedocs.io/en/stable/user/blobs/)</a></p> <pre><code><CLUSTER>_<BINARY ID>_lgQ_period.mrt</code></pre> <p>The 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary as a function of tidal period</p> <pre><code><CLUSTER>_<BINARY ID>_burnin_period.mrt</code></pre> <p>The MCMC burn-in period before the 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary are considered converged (see article text).</p> <pre><code><CLUSTER>_<BINARY ID>_cdfstd_period.mrt</code></pre> <p>The standard deviation of the <span class="math-tex">\(CDF(\log_{10}Q_\star')\)</span> for the given binary as a function of tidal period for each of the quantiles. The maximum likelihood value is the target percentile, i.e. one of: 2.3%, 15.9%, 84.1%, and 97.7%</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Data from: Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage
<p>We investigated the effect of storage duration and conditions on the assessment of the soil biota with eDNA metabarcoding. We extracted eDNA from freshly collected soil samples and again from the same samples after storage under contrasting temperature conditions and contrasting exposure (open/closed tubes). We used four different primer sets targeting bacteria, fungi, protists (cercozoans), and general eukaryotes. <span>W</span>e quantified differences in richness, evenness, and community composition. Subsequently, we tested whether we could correctly infer habitat type and original sample identity after storage using a large reference dataset.</p> <p>This repository contains the un-demultiplexed fastq sequences.</p>
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
Figure 3. Data Integration Sample-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Pentaho Data Integration has implemented a metadata-driven approach where you<br> only specify the data you want integrated, but you do not specify the way you want it done.<br> One of the most important advantages of Pentaho is that one can create complex<br> transformations and jobs in a graphical, drag-and-drop environment without having to create<br> proprietary custom code that will work only with some proprietary application.</p>
R code and data for: How much is enough? Minimum sample sizes in community ecology
<p>minimum_sample_sizes_code.R includes the complete set of instructions used to carry out the analyses in the related manuscript, plus save the computed data files and generate the text figures. data_files.tar.gz includes all of the files generated by the R code.</p>
single cell RNA seq data of 3 healthy sample from frontal lobe and temporal lobe
<p><span>Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We provided single-nucleus transcriptome analysis in “fresh” human FL and TL which are integrated with genetic susceptibility, gene dysregulation in neuropsychiatric disease, and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB-neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. these data provide prefound insight into brain frontotemporal lobe.</span></p>
Data from: Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA
<p><span>Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m<sup>3</sup> of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.</span></p> <p><span>The database is organized in five datasets in a csv format (columns separated by commas): (1) metadata providing the location, date, and time for each sample, along with sequencing depth and other essential information (metadata.csv); (2) species-level OTU tables per sample describing the number of sequences assigned to each species (otu.table.csv 3); (3) taxonomic classification of each species-level OTU (taxonomy.csv); (4) closest matching sequences and their taxonomy for ASVs in putatively fungal pseudophyla, which are included in (2) and (3) (fungi_pseudophyla.csv); and (5) closest matching sequences and their taxonomy for ASVs in putatively non-fungal pseudophyla, which are not included in the other datasets (nonfungi_pseudophyla.csv). The first four datasets can be linked to each other using the unique sample codes and the unique identifiers for species-level OTUs. </span><span>The three first datafiles are also provided in allData.RData which can be read into R as load("allData.RData").</span></p>
Data sample for Mercury simulator
<p><strong>Sample data to run Mercury</strong></p> <p>Mercury is available at:<strong> </strong><a href="https://github.com/UoW-ATM/Mercury">https://github.com/UoW-ATM/Mercury</a></p> <p>This file contains a sample of input data for the open-source air mobility simulator Mercury. Please use use version 3 of the dataset for Mercury 3.0 and version X.Y.Z for Mercury X.Y.</p> <p>The dataset is structured as follows:</p> <ul> <li><strong>input</strong>: Input folder for Mercury <ul> <li><strong>input/scenario=-1</strong>: Folder containing scenario -1 with about 1000 flights anonymised.</li> <li><strong>input/scenario=-1/scenario_config.toml</strong>: Configuration file for the scenario</li> <li><strong>input/scenario=-1/data</strong>: data provided is organised as follows: <ul> <li><strong>ac_performance</strong>: aircraft performance dataset. Requires BADA files (not provided, BADA files need to be structured inside provided folders (see Mercury Readme)</li> <li><strong>airlines</strong>: static information on airlines used in the scenario</li> <li><strong>airports</strong>: static information on airports, including capacity declarations and minimum turnaround time. Two subfolders included: taxi (with taxi-in and taxi-out times) and curfew (with curfew times)</li> <li><strong>costs</strong>: data required to define cost functions</li> <li><strong>delay</strong>: delay parameters (non-ATFM)</li> <li><strong>eaman</strong>: definition of EAMAN in scenario (scope)</li> <li><strong>flight_plans</strong>: information on the flight plans, contains: <ul> <li><strong>crco</strong>: not provided as not needed to run Mercury (used for FP generation)</li> <li><strong>en_route_wind</strong>: not provided as not needed to run Mercury (used for FP generation)</li> <li><strong>flight_plans_pool</strong>: pool of flight plans for o-d ac type triplets</li> <li><strong>flight_uncertainty</strong>: distributions to model uncertainty on the realisation of the flight plans</li> <li><strong>routes</strong>: routes available between o-d pairs (used only for HMI and for FP generation (not needed to run Mercury))</li> <li><strong>trajectories</strong>: trajectories available from o-d ac type triplets (used only for HMI and for FP generation (not needed to run Mercury))</li> <li><strong>network_manager</strong>: ATFM probabilities, distributions and definition</li> </ul> </li> <li><strong>pax</strong>: passenger itineraries for flights provided</li> <li><strong>scenario</strong>: static information on scenario (to be deprecated in subsequent updates)</li> <li><strong>schedules</strong>: flight schedules provided (note these will determine which airports, flight plans, etc. are provided in the dataset)</li> </ul> </li> <li><strong>input/scenario=-1/case_sdudies</strong>: folder to contain the definition of case studies <ul> <li><strong>case_study=0</strong>: default case study with the configuration file (case_study_config.toml)</li> </ul> </li> </ul> </li> </ul> <p> </p>
FIG. 6 in Concentration data of (+)-usnic acid enantiomer from some European and African samples of Flavoparmelia caperata (L.) Hale (Parmeliaceae, lichenised Ascomycota) - results of a preliminary study
FIG. 6. — Usnic acid concentrations (mg g–1 dry weight) in the lichen Flavoparmelia caperata (L.) Hale measured in Europe and Africa by HPLC-PDA. The lines represent the minimum and maximum values, the box represents the 25 and 75% of the data, the thick line represents the median. Continents are not significantly different at 95% confidence.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.