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Experts predict that the climate change will affect all natural and human systems. Historical climate records show changes in average and extreme temperatures, as well as an increase in annual climate variability; this changes can affect all continents of our planet in different ways and the high levels of poverty in some regions can worsen the impacts of climate change. In addition, families who are dedicated to making agriculture and owning small land units directly dependent on its production to keep your family economy. The purpose of this research was to identify levels of vulnerability of coffee farming families in four countries of the Mesoamerican Region (Nicaragua, Guatemala, El Salvador and Mexico), because coffee is an important livelihood for thousands of families in this region. In order to know the response of crops to projected changes, the state of their resources and ultimately the strategies that they can implement to minimize the impact of climate change, this research was done using climate models to project the coffee climate suitability and the exposure in this regions; also qualitative tools were combined under the focus on livelihoods and resources of families to identify the vulnerability level.
Data Types:
  • Tabular Data
  • File Set
Data products to support reproduction of the first interferometric localization of Fast Radio Burst 121102. First publication available at http://dx.doi.org/10.1038/nature20797 and data analysis recipes are available at http://github.com/caseyjlaw/FRB121102. The principle data products are nine tar files of fast-sampled interferometric data from the Very Large Array. Each observation segment includes 4 seconds (800 integrations) of visibilities centered on a radio burst from FRB 121102. Data is in science data model (SDM) format, which consists of a directory with metadata in XML files and binary data in the ASDMBinary subdirectory. Binary data were recorded with an integration time of 5 ms, frequencies at S-band (2.5-3.5 GHz; 256 channels), and dual-circular polarizations. Each file is identified by a unique name of the observation, plus the observation start time in MJD. Each visibility data set has an associated calibration file (telcal format; ending in ".GN") that can be parsed to apply gain calibration. The Python package rtpipe (https://github.com/caseyjlaw/rtpipe) can perform all calibration and analysis of these data. This library is supported by sdmpy (https://github.com/demorest/sdmpy), a Python library to read SDM files, and pwkit (https://github.com/pkgw/pwkit), a Python interface to the CASA data analysis package. See also http://realfast.io for information on the project behind the data acquisition system and analysis software.
Data Types:
  • Other
  • File Set
Replication code and sample data used for above mentioned publication in Political Analysis.
Data Types:
  • Software/Code
  • Geospatial Data
  • Document
  • File Set
Additional Data for the publication "Absolute Configuration of Native Oligomeric Proanthocyanidins with Dentin Biomodification Potency" in the Journal of Organic Chemistry
Data Types:
  • File Set
We created land fragmentation datasets in 1991 and 2000 for forests, urban areas, and wetlands according to the corresponding land cover maps (Kennaway and Helmer, 2007). Spatial resolution: 3 km; Temporal periods: 1991 and 2000.
Data Types:
  • File Set
We simulated the coastal wetlands migration under sea level rise scenarios using the Sea Level Affecting Marshes Model (SLAMM). The input DEM is at the spatial resolution of 5 m created from Lidar data accessible at Digital Coast website. The wetland distribution is according to the National Wetland Inventory and the wetlands classification was modified to fit that in SLAMM manual. The developed areas were delineated according to the aerial photos taken in 2010 at the spatial resolution of 0.4 m. The wetland migration maps are at the spatial resolution of 5 m.
Data Types:
  • File Set
This folder contains the contents of a project examining the relationship between demographics and nearby early vote locations in the 2016 and 2012 elections in North Carolina. FILES: * derived: This folder contains data produced by nc_evote_analysis.do * maptile: This folder contains the files needed to define a “ncvtd” (North Carolina Voting Tabulation District) geography for the Maptile Stata program. Once Maptile is installed, drag these files to the same folder as the Maptile ado file in your personal ado folder. * nc_evote_analysis.do: The Stata DO-File that processes raw data and produces results * source: This folder contains the source data for the project. Some of these files are raw downloads, while others were created manually from a combination of inputs * tables_figures: This folder contains tables and figures produced by nc_evote_analysis.do
Data Types:
  • Software/Code
  • Geospatial Data
  • Tabular Data
  • Document
  • Text
  • File Set
V6 All Layers for 1820 in GBK Encoding: Counties, Prefectures, Provinces, Towns, Rivers, Lakes.
Data Types:
  • Text
  • File Set
The CHGIS version of CITAS 1990 Counties, combines basic Population Census Variables with GIS Data County Level, released under agreement with CIESIN. Original Data Distributed by: SEDAC CIESIN URL: http://sedac.ciesin.columbia.edu/data/collection/cddc
Data Types:
  • Text
  • File Set
V6 All Layers for 1820 in GBK Encoding: Counties, Prefectures, Provinces, Towns.
Data Types:
  • Text
  • File Set
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