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1,079 results for “source data”
Updated example thaumatin data set from Diamond Light Source VMXi
<p>Example data set from the VMXi beamline at Diamond Light Source recorded in situ from a thaumatin crystal with processing results:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/NATIVE Overall Low High High resolution limit 1.85 5.02 1.85 Low resolution limit 50.49 50.50 1.88 Completeness 68.0 100.0 2.7 Multiplicity 3.3 3.9 1.0 I/sigma 12.9 24.8 1.4 Rmerge(I) 0.056 0.031 0.386 Rmerge(I+/-) 0.047 0.027 0.000 Rmeas(I) 0.065 0.035 0.545 Rmeas(I+/-) 0.061 0.035 0.000 Rpim(I) 0.032 0.017 0.386 Rpim(I+/-) 0.038 0.022 0.000 CC half 0.998 0.999 0.000 Wilson B factor 20.100 Anomalous completeness 58.0 99.8 0.1 Anomalous multiplicity 1.9 2.4 1.0 Anomalous correlation -0.015 -0.107 0.000 Anomalous slope 0.928 Total observations 53105 5240 32 Total unique 15898 1349 31 Assuming spacegroup: P 41 21 2 Other likely alternatives are: P 43 21 2 Unit cell (with estimated std devs): 58.6330(3) 58.6330(3) 151.4573(10) 90.0 90.0 90.0 </pre>
Source Data Images for Figures 3a-d, Fig 4
<p>Source Data Files for Figures 3a-d, 4d</p> <p><strong>STAT5B<sup>N642H</sup> transforms T-cell subsets, resulting in differential peripheral organ infiltration.</strong></p> <p>Histological analysis using CD3, Ki67 and H&E staining of the skin, lung, liver and brain of 7- to 9-week-old wild type (WT), human STAT5B and human STAT5B<sup>N642H</sup> mice. Images are representative of three independent experiments. Original magnification: 4x (left panels in C and D), 20× and 40× (insets), scale bars = 100 μm. These images represent Source Data files for Figures 3a-d and 4d.</p> <p>Histological analysis using CD3, Ki67 and H&E staining of the liver of recipient mice transplanted with γδ T-cells from hSTAT5B<sup>N642H</sup> (n = 2) or WT (n = 1) mice. Original magnification: 20× and 40× (insets), scale bars = 100 μm.</p>
Radiolaria specimens from Korsfjorden Nov 1969 - May 1971 source data
<p>Originally published on GBIF.org - https://doi.org/10.15468/jhkb5u</p> <p>This dataset is the result of a radiolarian sampling program from Korsfjorden (60.19110 N,5.23136 E), on the west coast of Norway near Bergen, that took place from November 1969 and ended in May 1971. At the sampling location the fjord depth is 670 m. The water column was divided into 100 m depth zones (100-0 m, 200-100 m, ….600-500 m, and 650-600 m) except for the deepest zone where the sampling started ca. 20 m above the mud line to avoid contamination of the net. At each sampling date all depth zones were sampled. A plankton net with 63 µm mesh opening was used. This dataset also includes the phaeodarians found in this dataset. A total of 72 species was observed. A discussion of this dataset can be found in: </p> <p>Bjørklund, K.R., 1974. The seasonal occurrence and depth zonation of radiolarians in Korsfjorden, Western Norway. Sarsia, 56: 13-42.</p>
Source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice"
<p>This repository contains the source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice" currently under review by PLOS ONE.</p> <p>It supports the following figures:</p> <p>Fig 4A: rHA ELISA data miniHA study.xlsx<br> Fig 4B: Competition ELISA data miniHA study.xlsx<br> S7 Fig: Competition ELISA data POC study.xlsx</p> <p> </p> <p>Files include Raw OD's per plate and reported values analysis. </p> <p> </p>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Simulation data and source code for Hausdorff dimension measurements in two-dimensional quantum gravity
<p>This entry contains the source code and simulation data used as basis for the paper</p> <p>J. Barkley, T. Budd, "Precision measurements of Hausdorff dimensions in two-dimensional quantum gravity." Preprint <a href="https://arxiv.org/abs/1908.09469">arXiv:1908.09469</a> (2019)</p> <p>Both the source code and the data consist of two parts:</p> <ul> <li>Measurements of (dual) graph distances in various models of random planar maps.</li> <li>Measurements of discrete Liouville first passage percolation distances on a regular lattice with periodic boundary conditions.</li> </ul> <p>Instructions on compiling and running the simulation software are included with the source code (see README files). Descriptions of the simulation data formats accompany the data files (see README files again). For background on the simulation and data analysis we refer to the publication mentioned above.</p>
Small example Eiger 2X 16M data set from Diamond Light Source I04
<p>Useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded from a thaumatin crystal (unfortunate naming on my part) </p> <p> </p> <p>Revision includes screening images, and addition of name dataset in /entry/instrument</p> <p> </p> <pre>> h5dump -d /entry/instrument/name Therm_6_2.nxs HDF5 "Therm_6_2.nxs" { DATASET "/entry/instrument/name" { DATATYPE H5T_STRING { STRSIZE H5T_VARIABLE; STRPAD H5T_STR_NULLTERM; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "Macromolecular Crystallography I04" } } } </pre>
Geochemical data of fine bed-sediment from downstream sediment cores and upstream source sub-catchments in a catchment-wide flood in the Brantian
<b>Description: </b><p>Geochemical datasets were obtained from fieldwork carried out in the Brantian catchment between June 2013 and November 2016 under the hydrology component of the SAFE Project. The project has two key components: (1) Geochemical profiles down historical sediment cores at seven downstream locations organised in a nested hierarchical arrangement; and (2) Geochemical data for sediment deposited by the single extreme high magnitude flood event of 12 September 2016 in all sub-catchment source areas sampled around the Brantian including all downstream sediment core locations referred to in (1) <br>Sample collection and preparation method:<br>Fluvial sediment cores were obtained from seven downstream sites located within a nested hierarchical (dendritic) arrangement with the study catchment outlet draining 377 km2 of the upper Brantian. Core sites 4 and 5 in the west were nested within core site 2; core sites 6 and 7 in the east were nested within core site 3; core sites 2 and 3 were in turn nested within core site 1 at the study catchment outlet. Areas upstream at each drainage hierarchy varied from 30-135km2 (core sites 4-7); 150-200km2 (core sites 2-3) and 377km2 (core site 1).<br>Sediment cores were obtained within the bankfull channel at sites inundated by high-flow events with the progressive accumulation of fine bed-sediment monitored by repeat measurements of surface profile. Pits were dug to create a shelf surface from which to obtain large (200g to 1100g) bulk samples of sediment integrated over depth intervals of 2cm. The shelf technique permits larger samples while depths are absolute and not affected by core liner compression. Core depths ranged from 102 cm to 210 cm.<br>Sediment samples deposited in the high-flow event of 12 September 2016 were obtained using pre-installed surface horizon marker grids. Surface-layer (0-2cm) scrape samples were composited over a 10-20 m2 area. At sites with depths of fresh sediment >2cm small sediment cores representing sediment deposited in that event were obtained using the shelf technique. Field replicates were obtained from the same elevation and at either higher or lower elevation within the channel.<br>All sediment samples were oven-dried at temperatures no higher than 40 C before dry-sieving to obtain the fine-sediment <63um size fraction. Other size fractions were obtained from nested sieve stacks in order to calculate bulk particle size distribution from the entire sample. Samples of dried <63um sediment were thoroughly mixed by hand (not ground) before a sub-sample transferred to a standard 32mm outer-diameter plastic pot pre-fitted with a 3um thick Prolene XRF analytical film window, compacted to 20Nm torque pressure and sealed. Mass (g) and total thickness (mm) of prepared samples were recorded. <br>Geochemical analysis method:<br>Total elemental concentration of each sample (ppm) was measured using a Niton XL3t GOLDD+ 900 Energy-Dispersive X-Ray Fluorescence (ED-XRF) analyser in a laboratory stand with count periods of 180 seconds for 'soils' (Compton scatter) mode (60s in each of three energy band filters: low medium and high). Measurements were also made in 'mining' mode (Fundamental Parameters calibration) using helium purging to obtain concentrations of light elements Mg-S. In both modes, two measurements were obtained for each sample by repositioning the sample window exposed to the XRF beam after the first measurement (position 1 and position 2). <br>ED-XRF analysis returns total elemental concentration (ppm) of elements Mg-U. Measurement error is reported by the Niton XRF in terms of 2sigma (two times standard deviation) for each element. All measurements were higher than limit of detection. Variability between the two measurement positions 1 and 2 reflects geochemical environmental variability in sediment (ie sampling error) and analytical error.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/133"><b>Assessing erosional impacts of logging and conversion to oil palm in the Brantian catchment using sediment fingerprinting and radioisotope dating.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Doctoral Training Grant, NE/L501827/1)</li><li>British Geomorphological Society (Postgraduate Research Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3(149))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.5(145))</li><li>Sabah Forestry Department (Research licence 100-14/18/2KLT.29(37))</li><li>Maliau Basin Management Committee (MBMC) (Research licence 2015/29(165))</li><li>Sabah Biodiversity Council (Export licence JKM/MBS.1000-2/3 JLD.2(86))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3402746">here</a></p><p><b>Files: </b>This consists of 1 file: SamHigton_Geochem_fluvial_sediment_data.xlsx</p><p><b>SamHigton_Geochem_fluvial_sediment_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Sediment core geochemistry data</b> (described in worksheet Sediment_core_geochem_data)</p><p>Description: XRF analysis data of geochemical composition of sediment from 7 core sites. There is one sample for every 2cm depth interval down each core (with one set of bulk particle size data) and then two separate repeat XRF measurements of each sample.</p><p>Number of fields: 69</p><p>Number of data rows: 947</p><p>Fields: </p><ul><li><b>Core_number</b>: Core number (Field type: id)</li><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Upstream_Area_km2</b>: Upstream area (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li><li><p><b>Sept2016 flood geochemical data</b> (described in worksheet Sept2016_flood_geochem_data)</p><p>Description: XRF analysis of geochemical composition of sediment from a single flood event around the Brantian. Most samples have depth of 0-2cm since this represents surface layer material, but at many sites there were also 'mini' cores which is why the depths vary. 28 elements analysed as above and each sample measured twice, with error columns and bulk particle size etc.</p><p>Number of fields: 70</p><p>Number of data rows: 222</p><p>Fields: </p><ul><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Site_Type</b>: Site Type - U: Upstream site (sample taken at one of the upstream source sites 9 to 29) ; C: Core site (sample at a sediment core site which are only sites numbered 1 to 7); RC: Field replicate from the sediment core site itself; RL: Field replicate at that site but from a relatively lower elevation position; RH: Field replicate at that site but from a relatively higher elevation position) (Field type: categorical)</li><li><b>Upstream_Area_km2</b>: Total drainage area upstream of each sample site (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-1mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_1mm-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Thickness of prepared sample for XRF analysis (to nearest 0.5cm) (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2013-06-21 to 2019-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Source data files
<p>Source data files for: "Alternative transcription cycle for bacterial RNA polymerase"</p> <p>Timothy T. Harden<sup>1</sup>, Karina S. Herlambang<sup>2†</sup>, Mathew Chamberlain<sup>1†</sup>, Jean-Benoît Lalanne­<sup>3,4</sup>, Christopher D. Wells<sup>5</sup>, Gene-Wei Li<sup>3</sup>, Robert Landick<sup>6</sup>, Ann Hochschild<sup>5</sup>, Jane Kondev<sup>1</sup>, and Jeff Gelles<sup>2</sup></p> <p>Departments of <sup>1</sup>Physics and <sup>2</sup>Biochemistry, Brandeis University, Waltham, MA 02454, USA</p> <p>Departments of <sup>3</sup>Biology and <sup>4</sup>Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA</p> <p><sup>5</sup>Department of Microbiology, Blavatnick Institute, Harvard Medical School, Boston, MA, 02115 USA</p> <p><sup>6</sup>Department of Biochemistry and Department of Bacteriology, University of Wisconsin, Madison, WI 53706, USA</p> <p>†These authors contributed equally and are listed in the order determined by a coin toss.</p>
Small example Eiger 2X 16M data set from Diamond Light Source I04 revised for HDRMX Gold Standard Discussion
<p>Revised useful small (488 frame) Eiger data set recorded during routine testing, useful for software testing as it is small. Data recorded is from a thaumatin crystal by Graeme Winter, The original dataset is <a href="https://zenodo.org/record/3385862">https://zenodo.org/record/3385862</a> which contains two Eiger MX datasets, Therm_6_1 and Them_6_2, each with a data file and two versions each of the metadata -- a "..._master.h5" file and a "....nxs" file. The former are the usual Eiger metadata files using exposed external links to connect the metadata to the date, and the latter are HDF5-1.10 VDS files. This revision has the same data as the original Therm_6_2 data, but now includes with the "master.h5" file a "..._master_rev.h5" file and with the ".nxs" file a "..._rev.nxs" VDS file.</p> <p>The purpose to the changes in the "..._rev" files is to provide a supporting example for the HDRMX discusssion of a new proposed Eiger "gold standard" to improve the ability to process Eiger MX data collected at one facility at other facilties, by ensuring that sufficient metadata is stored with all datasets.</p> <p>The changes were made by the following script</p> <pre>cp Therm_6_2.nxs Therm_6_2_rev.nxs cp Therm_6_2_master.h5 Therm_6_2_master_rev.h5 export LD_LIBRARY_PATH=$HOME/lib export HDF5_PLUGIN_PATH=$HOME/lib export PATH=$HOME/bin:$PATH h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/name -d /entry/instrument/name -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_master_rev.h5 -s /entry/instrument/source -d /entry/source -f ref h5copy -i Therm_6_2_rev.nxs -o Therm_6_2_rev.nxs -s /entry/instrument/source -d /entry/source -f ref export end_time=`h5dump -d "/entry/end_time" Therm_6_2_master.h5 | grep ":" | sed 's/^.........//'|sed 's/.\$//'` echo "end_time: $end_time" python << 'EOL' import h5py import numpy as np import os end_time=os.environ['end_time'] fvds = h5py.File('Therm_6_2_rev.nxs','r+') fmaster = h5py.File('Therm_6_2_master_rev.h5','r+') fvds_keys=fvds.keys() fmaster_keys=fmaster.keys() fvds_entry=fvds['entry'] fmaster_entry=fmaster['entry'] fvds_entry_keys=fvds_entry.keys() fmaster_entry_keys=fmaster_entry.keys() fvds_entry_instrument=fvds['entry']['instrument'] fmaster_entry_instrument=fmaster['entry']['instrument'] fvds_entry_instrument_keys=fvds_entry_instrument.keys() fmaster_entry_instrument_keys=fmaster_entry_instrument.keys() fvds_entry_instrument_name=(fvds['entry']['instrument']['name']) fmaster_entry_instrument_name=(fmaster['entry']['instrument']['name']) fvds_entry_instrument_short_name=fvds_entry_instrument.attrs['short_name'] fmaster_entry_instrument_short_name=fmaster_entry_instrument.attrs['short_name'] zero_offset=fmaster_entry_instrument['detector']['module']['fast_pixel_direction'].attrs['offset'] fmaster_det_z=fmaster_entry_instrument['transformations']['det_z'] fvds_det_z=fvds_entry_instrument['transformations']['det_z'] print('fvds_keys: ',fvds_keys) print('fmaster_keys: ',fmaster_keys) print('fvds_entry_keys: ',fvds_entry_keys) print('fmaster_entry_keys: ',fmaster_entry_keys) print('fvds_entry_instrument_keys: ',fvds_entry_instrument_keys) print('fmaster_entry_instrument_keys: ',fmaster_entry_instrument_keys) print('fvds_entry_instrument_name: ',fvds_entry_instrument_name) print('fmaster_entry_instrument_name: ',fmaster_entry_instrument_name) print('fvds_entry_instrument_short_name: ',fvds_entry_instrument_short_name) print('fmaster_entry_instrument_short_name: ',fmaster_entry_instrument_short_name) print('fmaster_entry_instrument_detector_module_fast_pixel_direction_offset: ',zero_offset) print('fmaster_entry_instrument_detector_detector_z_det_z: ',fmaster_det_z) print('fmaster_entry_end_time: ',end_time) fmaster.attrs.modify('file_time',np.string_(end_time)) fmaster.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fmaster.attrs.modify('HDF5_Version',np.string_('hdf5-1.8.18')) fvds.attrs.modify('file_time',np.string_(end_time)) fvds.attrs.modify('file_name',np.string_('Therm_6_2_master_rev.h5')) fvds.attrs.modify('HDF5_Version',np.string_('hdf5-1.10.5')) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fmaster_entry_instrument_name.attrs.modify('short_name',np.string_(fmaster_entry_instrument.attrs['short_name'])) fmaster_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fmaster_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fvds_entry_instrument_name.attrs.modify('short_name',np.string_(fvds_entry_instrument.attrs['short_name'])) fvds_entry_instrument['attenuator']['attenuator_transmission'].attrs.modify('units',np.string_("")) fvds_entry_instrument['detector']['count_time'].attrs.modify('units',np.string_("s")) fmaster_det_z.attrs.modify('offset',zero_offset) fvds_det_z.attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fmaster_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['phi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['chi'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_x'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_y'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['sam_z'].attrs.modify('offset',zero_offset) fvds_entry['sample']['transformations']['omega'].attrs.modify('offset',zero_offset) print(fmaster['entry']['instrument']['name'].attrs['short_name']) print(fmaster['entry']['instrument']['name'].attrs['short_name'].shape) print(fmaster['entry']['instrument']['name'].attrs['short_name'].dtype) del fvds_entry_instrument.attrs['short_name'] del fmaster_entry_instrument.attrs['short_name'] del fmaster_entry_instrument['source'] fvds.close() fmaster.close() quit() EOL $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_master_rev.h5 $HOME/bin/nxvalidate -a NXmx -l /home/yaya/hdrmx_rev_29Sep19/hdrmx/definitions Therm_6_2_rev.nxs </pre> <p>The revised cnxvalidate and definitions are available on github</p> <p><a href="https://github.com/HDRMX/cnxvalidate.git">https://github.com/HDRMX/cnxvalidate.git</a></p> <p><a href="https://github.com/HDRMX/definitions.git">https://github.com/HDRMX/definitions.git</a></p> <p> </p> <p> </p>
Source Data file for Weiss et al., Nature Communications 10, 4772 (2019).
<p>Source Data file for Weiss et al., "Controlled Creation of a Singular Spinor Vortex by Circumventing the Dirac Belt Trick", Nature Communications <strong>10</strong>, 4772 (2019); containing data for all relevant figures.</p>
Part 2 of real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"
<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season. Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible. Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper. Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties. The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors). These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
Real-time testing data for: "Identifying data sources and physical strategies used by neural networks to predict TC rapid intensification"
<p>Each file in the dataset contains machine-learning-ready data for one unique tropical cyclone (TC) from the real-time testing dataset. "Machine-learning-ready" means that all data-processing methods described in the journal paper have already been applied. This includes cropping satellite images to make them TC-centered; rotating satellite images to align them with TC motion (TC motion is always towards the +x-direction, or in the direction of increasing column number); flipping satellite images in the southern hemisphere upside-down; and normalizing data via the two-step procedure.</p> <p>The file name gives you the unique identifier of the TC -- e.g., "learning_examples_2010AL01.nc.gz" contains data for storm 2010AL01, or the first North Atlantic storm of the 2010 season. Each file can be read with the method `example_io.read_file` in the ml4tc Python library (https://zenodo.org/doi/10.5281/zenodo.10268620). However, since `example_io.read_file` is a lightweight wrapper for `xarray.open_dataset`, you can equivalently just use `xarray.open_dataset`. Variables in the table are listed below (the same printout produced by `print(xarray_table)`):</p> <p>Dimensions: (<br> satellite_valid_time_unix_sec: 289,<br> satellite_grid_row: 380,<br> satellite_grid_column: 540,<br> satellite_predictor_name_gridded: 1,<br> satellite_predictor_name_ungridded: 16,<br> ships_valid_time_unix_sec: 19,<br> ships_storm_object_index: 19,<br> ships_forecast_hour: 23,<br> ships_intensity_threshold_m_s01: 21,<br> ships_lag_time_hours: 5,<br> ships_predictor_name_lagged: 17,<br> ships_predictor_name_forecast: 129)<br>Coordinates:<br> * satellite_grid_row (satellite_grid_row) int32 2kB ...<br> * satellite_grid_column (satellite_grid_column) int32 2kB ...<br> * satellite_valid_time_unix_sec (satellite_valid_time_unix_sec) int32 1kB ...<br> * ships_lag_time_hours (ships_lag_time_hours) float64 40B ...<br> * ships_intensity_threshold_m_s01 (ships_intensity_threshold_m_s01) float64 168B ...<br> * ships_forecast_hour (ships_forecast_hour) int32 92B ...<br> * satellite_predictor_name_gridded (satellite_predictor_name_gridded) object 8B ...<br> * satellite_predictor_name_ungridded (satellite_predictor_name_ungridded) object 128B ...<br> * ships_valid_time_unix_sec (ships_valid_time_unix_sec) int32 76B ...<br> * ships_predictor_name_lagged (ships_predictor_name_lagged) object 136B ...<br> * ships_predictor_name_forecast (ships_predictor_name_forecast) object 1kB ...<br>Dimensions without coordinates: ships_storm_object_index<br>Data variables:<br> satellite_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_number (satellite_valid_time_unix_sec) int32 1kB ...<br> satellite_band_wavelength_micrometres (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_cyclone_id_string (satellite_valid_time_unix_sec) |S8 2kB ...<br> satellite_storm_type_string (satellite_valid_time_unix_sec) |S2 578B ...<br> satellite_storm_name (satellite_valid_time_unix_sec) |S10 3kB ...<br> satellite_storm_latitude_deg_n (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_longitude_deg_e (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_intensity_number (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_u_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_storm_v_motion_m_s01 (satellite_valid_time_unix_sec) float64 2kB ...<br> satellite_predictors_gridded (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column, satellite_predictor_name_gridded) float64 474MB ...<br> satellite_grid_latitude_deg_n (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_grid_longitude_deg_e (satellite_valid_time_unix_sec, satellite_grid_row, satellite_grid_column) float64 474MB ...<br> satellite_predictors_ungridded (satellite_valid_time_unix_sec, satellite_predictor_name_ungridded) float64 37kB ...<br> ships_storm_intensity_m_s01 (ships_valid_time_unix_sec) float64 152B ...<br> ships_storm_type_enum (ships_storm_object_index, ships_forecast_hour) int32 2kB ...<br> ships_forecast_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_forecast_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_v_wind_200mb_0to500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vorticity_850mb_0to1000km_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_latitude_deg_n (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_vortex_longitude_deg_e (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_0to600km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_max_tangential_wind_850mb_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_1000mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_850mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_500mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_mean_tangential_wind_300mb_at500km_m_s01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to700mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_srh_1000to500mb_200to800km_j_kg01 (ships_storm_object_index, ships_forecast_hour) float64 3kB ...<br> ships_threshold_exceedance_num_6hour_periods (ships_storm_object_index, ships_intensity_threshold_m_s01) int32 2kB ...<br> ships_v_motion_observed_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_1000to100mb_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_v_motion_optimal_flow_m_s01 (ships_storm_object_index) float64 152B ...<br> ships_cyclone_id_string (ships_storm_object_index) object 152B ...<br> ships_storm_latitude_deg_n (ships_storm_object_index) float64 152B ...<br> ships_storm_longitude_deg_e (ships_storm_object_index) float64 152B ...<br> ships_predictors_lagged (ships_valid_time_unix_sec, ships_lag_time_hours, ships_predictor_name_lagged) float64 13kB ...<br> ships_predictors_forecast (ships_valid_time_unix_sec, ships_forecast_hour, ships_predictor_name_forecast) float64 451kB ...</p> <p>Variable names are meant to be as self-explanatory as possible. Potentially confusing ones are listed below.</p> <ul> <li>The dimension ships_storm_object_index is redundant with the dimension ships_valid_time_unix_sec and can be ignored.</li> <li>ships_forecast_hour ranges up to values that we do not actually use in the paper. Keep in mind that our max forecast hour used in machine learning is 24.</li> <li>The dimension ships_intensity_threshold_m_s01 (and any variable including this dimension) can be ignored.</li> <li>ships_lag_time_hours corresponds to lag times for the SHIPS satellite-based predictors. The only lag time we use in machine learning is "NaN", which is a stand-in for the best available of all lag times. See the discussion of the "priority list" in the paper for more details.</li> <li>Most of the data variables can be ignored, unless you're doing a deep dive into storm properties. The important variables are satellite_predictors_gridded (full satellite images), ships_predictors_lagged (satellite-based SHIPS predictors), and ships_predictors_forecast (environmental and storm-history-based SHIPS predictors). These variables are all discussed in the paper.</li> <li>Every variable name (including elements of the coordinate lists ships_predictor_name_lagged and ships_predictor_name_forecast) includes units at the end. For example, "m_s01" = metres per second; "deg_n" = degrees north; "deg_e" = degrees east; "j_kg01" = Joules per kilogram; ...; etc.</li> </ul>
Data for "Source Altitude of Energetic In-cloud Pulses Inside Thunderstorms and Implication for the Intrinsic Brightness of Terrestrial Gamma-Ray Flashes"
<p>The spreadsheet contains detailed information for the EIP events analyzed in this work, including the time, location, peak current, distance to sensors, estimated source height, height error, etc. </p> <p>The compressed file in this dataset contains the low-frequency waveforms of EIPs measured by Duke and FIT sensors. The modeled skywaves are shown in the figures. The cross-correlation coefficient between the measured skywaves and the modeled waveforms of each event are also presented. </p> <p> </p>
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Source data for the publication "Single-shot latched readout of a quantum dot qubit using barrier gate pulsing"
<p>This repository contains data and source code for the publication "Single-shot latched readout of a quantum dot qubit using barrier gate pulsing."</p>
Source data of Waning snowfields have transformed into hotspots of greening within the alpine zone
<p>Source data of the figures and extended data figures of the paper entitled </p> <p><strong><span>Waning snowfields have transformed into hotspots of greening within the alpine zone</span></strong></p>
Bayesian Analysis of Paleotsunami Sources: Data and Stochastic Simulations
<p>This repository contains the datasets utilized in the research titled “Tracing the Sources of Paleotsunamis Using Bayesian Frameworks.” Each dataset is integral to the analysis and reconstruction efforts undertaken in the study.</p> <p> </p> <p><strong>File Descriptions</strong></p> <p> </p> <p><strong>1. CorrectedShoreline_jogan_deposit_data.csv</strong></p> <p> </p> <p>This file contains paleotsunami data collected by Sugawara et al. The data has been corrected to account for the shoreline position at the time of the paleotsunami event.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Sugawara, D., Goto, K., Imamura, F., Matsumoto, H., & Minoura, K. (2012). Assessing the magnitude of the 869 Jogan tsunami using sedimentary deposits: Prediction and consequence of the 2011 Tohoku-oki tsunami. <em>Sedimentary Geology, 282</em>, 14–26.</p> <p> </p> <p><strong>2. Stochastic_samples.zip</strong></p> <p> </p> <p>This archive contains the stochastic samples generated for the Japan Trench, utilizing the coupling distribution model from Loveless et al.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Loveless, J. P., & Meade, B. J. (2011). Spatial correlation of interseismic coupling and coseismic rupture extent of the 2011 Mw = 9.0 Tohoku-oki earthquake. <em>Geophysical Research Letters, 38</em>.</p> <p> </p> <p><strong>3. Selected_Stochastic_Samples.zip</strong></p> <p> </p> <p>This file includes a reduced sample space derived from the original stochastic samples, specifically selected for statistical analysis.</p> <p> </p> <p><strong>4. Sendai_1961.zip</strong></p> <p> </p> <p>This dataset contains the reconstructed morphology of the Sendai plain as it appeared in 1961. The reconstruction is based on aerial photographs provided by the Geospatial Information Authority of Japan (GSI).</p>
New particle formation from isoprene under upper-tropospheric conditions: data sources
<p>Data resources for manuscript: "<strong>New particle formation from isoprene under upper-tropospheric conditions</strong>"</p>
Compilation of mean monthly water table depth data (2015-2023) and linkages to further published sources of water table data, from European peatlands
<p>This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.</p>
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.