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Title: High-throughput imaging of heterogeneous cell organelles with an X-ray laser (CXIDB ID 25)

Preprocessed detector images that were used for the paper "High-throughput imaging of heterogeneous cell organelles with an X-ray laser". The CXI file contains the entire recorded data - including both hits and blanks. It also includes down-sampled images and LCLS machine parameters. Additionally, the Cheetah configuration file is attached that was used to create the pre-processed data.
Authors:
Publication Date:
Report Number(s):
CXIDB ID 25
DOE Contract Number:
AC02-05CH11231
Product Type:
Dataset
Research Org(s):
Coherent X-ray Imaging Data Bank (Lawrence Berkeley National Laboratory); Uppsala University
Sponsoring Org:
Uppsala University
Resource Relation:
Related Information: High-throughput imaging of heterogeneous cell organelles with an X-ray laser, Max F. Hantke et al. Nature Photonics, 2014, http://dx.doi.org/10.1038/nphoton.2014.270
Subject:
XFEL; AMO; Carboxysomes; X-ray Free-electorn Lasers; Single Particle X-ray Diffraction Imaging; LCLS
OSTI Identifier:
1169545
  1. The Coherent X-ray Imaging Data Bank (CXIDB) is a new database which offers scientists from all over the world a unique opportunity to access data from Coherent X-ray Imaging (CXI) experiments. The main goal of the Coherent X-ray Imaging Data Bank is to create an open repository for CXI experimental data. CXIDB is dedicated to further the goal of making data from Coherent X-ray Imaging (CXI) experiments available to all, as well as archiving it. The website also serves as the reference for the CXI file format, in which most of the experimental data on the database is stored in.
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  1. To help guide its future data collection efforts, The DOE GTO funded a data gap analysis in FY2012 to identify high potential hydrothermal areas where critical data are needed. This analysis was updated in FY2013 and the resulting datasets are represented by this metadata. Themore » original process was published in FY 2012 and is available here: https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2013/Esposito.pdf Though there are many types of data that can be used for hydrothermal exploration, five types of exploration data were targeted for this analysis. These data types were selected for their regional reconnaissance potential, and include many of the primary exploration techniques currently used by the geothermal industry. The data types include: 1. well data 2. geologic maps 3. fault maps 4. geochemistry data 5. geophysical data To determine data coverage, metadata for exploration data (including data type, data status, and coverage information) were collected and catalogued from nodes on the National Geothermal Data System (NGDS). It is the intention of this analysis that the data be updated from this source in a semi-automated fashion as new datasets are added to the NGDS nodes. In addition to this upload, an online tool was developed to allow all geothermal data providers to access this assessment and to directly add metadata themselves and view the results of the analysis via maps of data coverage in Geothermal Prospector (http://maps.nrel.gov/gt_prospector). A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to the five data types. Using these five data coverage maps and the USGS Resource Potential Map, sites were identified for future data collection efforts. These sites signify both that the USGS has indicated high favorability of occurrence of geothermal resources and that data gaps exist. The uploaded data are contained in two data files for each data category. The first file contains the grid and is in the SHP file format (shape file.) Each populated grid cell represents a 10k area within which data is known to exist. The second file is a CSV (comma separated value) file that contains all of the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. The attributes in the CSV include: 1. grid_id : The id of the grid cell that the data intersects with 2. title: This represents the name of the WFS service that intersected with this grid cell 3. abstract: This represents the description of the WFS service that intersected with this grid cell 4. gap_type: This represents the category of data availability that these data fall within. As the current processing is pulling data from NGDS, this category universally represents data that are available in the NGDS and are ready for acquisition for analytic purposes. 5. proprietary_type: Whether the data are considered proprietary 6. service_type: The type of service 7. base_url: The service URL « less
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