text:
include(vcpkg_common_functions)
vcpkg_download_distfile(ARCHIVE
    URLS "https://ftp.gnu.org/gnu/libiconv/libiconv-1.14.tar.gz"
    FILENAME "libiconv-1.14.tar.gz"
    SHA512 b96774fefc4fa1d07948fcc667027701373c34ebf9c4101000428e048addd85a5bb5e05e59f80eb783a3054a3a8a3c0da909450053275bbbf3ffde511eb3f387
)
vcpkg_extract_source_archive(${ARCHIVE})

#Since libiconv uses automake, make and configure, we use a custom CMake file
file(COPY ${CMAKE_CURRENT_LIST_DIR}/CMakeLists.txt DESTINATION ${CURRENT_BUILDTREES_DIR}/src/libiconv-1.14/)
file(COPY ${CMAKE_CURRENT_LIST_DIR}/LibiconvConfig.cmake.in DESTINATION ${CURRENT_BUILDTREES_DIR}/src/libiconv-1.14/)
file(COPY ${CMAKE_CURRENT_LIST_DIR}/new_config.h.in DESTINATION ${CURRENT_BUILDTREES_DIR}/src/libiconv-1.14/)

vcpkg_configure_cmake(
    SOURCE_PATH ${CURRENT_BUILDTREES_DIR}/src/libiconv-1.14

)

vcpkg_install_cmake()

# Handle copyright
file(COPY ${CURRENT_BUILDTREES_DIR}/src/libiconv-1.14/COPYING DESTINATION ${CURRENT_PACKAGES_DIR}/share/libiconv)
file(RENAME ${CURRENT_PACKAGES_DIR}/share/libiconv/COPYING ${CURRENT_PACKAGES_DIR}/share/libiconv/copyright)

# clean out the debug include
file(REMOVE_RECURSE ${CURRENT_PACKAGES_DIR}/debug/include)

repository: BenJamesbabala/vcpkg

repo_name: BenJamesbabala/vcpkg


text:
SUMMARY = "Repartition a safemode based installation to an A/B EFI implementation"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${COMMON_LICENSE_DIR}/MIT;md5=0835ade698e0bcf8506ecda2f7b4f302"

SRC_URI = "\
    file://nilrt-gateway-install \
"

PV = "${DISTRO_VERSION}"
RDEPENDS_${PN} += "bash"
ALLOW_EMPTY_${PN}-dbg = "0"
ALLOW_EMPTY_${PN}-dev = "0"
do_install[depends] = "restore-mode-image:do_image_complete"

do_install_append_x64() {

    install -d ${D}/usr/share/nilrt
    install -m 0755 ${WORKDIR}/nilrt-gateway-install ${D}/usr/share/nilrt/nilrt-install
    install -m 0755 ${DEPLOY_DIR_IMAGE}/restore-mode-image-${MACHINE}.wic ${D}/usr/share/nilrt/restore-mode-image-${MACHINE}.iso
}

python do_package_ipk_prepend() {
    d.setVar('OPKGBUILDCMD', 'opkg-build')
}

python do_package_ipk_append() {
    d.setVar('OPKGBUILDCMD', '')
}

FILES_${PN}_x64 = "\
    /usr/share/nilrt/restore-mode-image-${MACHINE}.iso \
    /usr/share/nilrt/nilrt-install \
"

license: MIT

repository: NIwkoe/meta-nilrt

license_type: permissive

repo_name: NIwkoe/meta-nilrt

branch_name: refs/heads/nilrt/master/sumo

gha_license_id: MIT

extension: bb


text:
@article{10.1371/journal.pntd.0006896,
    author = {Juarez, Jose G. AND Pennington, Pamela M. AND Bryan, Joe P. AND Klein, Robert E. AND Beard, Charles B. AND Berganza, Elsa AND Rizzo, Nidia AND Cordon-Rosales, Celia},
    journal = {PLOS Neglected Tropical Diseases},
    publisher = {Public Library of Science},
    title = {A decade of vector control activities: Progress and limitations of Chagas disease prevention in a region of Guatemala with persistent Triatoma dimidiata infestation},
    year = {2018},
    month = {11},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pntd.0006896},
    pages = {1-17},
    abstract = {Author summary Despite the success of the Chagas disease vector control program in Guatemala, there are still regions with persistent T. dimidiata infestation. The municipality of Comapa, Jutiapa, is one of such areas. After several rounds of residual insecticide sprayings, infestation of some communities remained >15%. We evaluated the impact of a decade of vector control activities through three cross-sectional seroprevalence and entomological surveys (1999 through 2015). We found that transmission to children has decreased ten-fold. However, new infections persist in women and children. To reach the Chagas disease elimination goal, integrated vector control strategies should include community-wide insecticide applications, education to change exposure to risk factors and increased access to diagnostics and treatment for women and children.},
    number = {11},
    doi = {10.1371/journal.pntd.0006896}
}

repository: jgjuarez/academic-kickstart

repo_name: jgjuarez/academic-kickstart

branch_name: refs/heads/master

extension: bib


text:
@article{guyon_building_2003,
 author = {Guyon, R. and Kirkness, E. F. and Lorentzen, T. D. and Hitte, C. and Comstock, K. E. and Quignon, P. and Derrien, T. and André, C. and Fraser, C. M. and Galibert, F. and Ostrander, E. A.},
 doi = {10.1101/sqb.2003.68.171},
 issn = {0091-7451},
 journal = {Cold Spring Harbor Symposia on Quantitative Biology},
 keywords = {Animals, Chromosome Mapping, Chromosomes, Human, Pair 1, Dogs, Genome, Humans, Radiation Hybrid Mapping, Species Specificity},
 language = {eng},
 pages = {171--177},
 pmid = {15338615},
 shorttitle = {Building comparative maps using 1.5x sequence coverage},
 title = {Building comparative maps using 1.5x sequence coverage: human chromosome 1p and the canine genome},
 volume = {68},
 year = {2003}
}


text:
.x-btn button.aloha-button-imageManager {
    background: url( '../img/image.png' );
}
.x-btn button.aloha-button-SVGManager {
    background: url( '../img/svg.png' );
}
.x-btn button.aloha-button-MathJaxButton {
    background: url( '../img/math.png' );
}
.x-btn button.aloha-button-CodeButton {
    background: url( '../img/code.png' );
}
.x-btn button.aloha-button-easyQuestion {
    background: url( '../img/question.png' );
}

repository: AKSW/SlideWiki

repo_name: AKSW/SlideWiki

gha_language: JavaScript

extension: css


text:
@INPROCEEDINGS{zhang14,
  author = {Zhang, Xiao-Lei},
  title = {Nonlinear Dimensionality Reduction of Data by Deep Distributed Random
	Samplings},
  pages = {221-233},
  abstract = {Dimensionality reduction is a fundamental problem of machine learning,
	and has been intensively studied, where classification and clustering
	are two special cases of dimensionality reduction that reduce high-dimensional
	data to discrete points. Here we describe a simple multilayer network
	for dimensionality reduction that each layer of the network is a
	group of mutually independent k-centers clusterings. We find that
	the network can be trained successfully layer-by-layer by simply
	assigning the centers of each clustering by randomly sampled data
	points from the input. Our results show that the described simple
	method outperformed 7 well-known dimensionality reduction methods
	on both very small-scale biomedical data and large-scale image and
	document data, with less training time than multilayer neural networks
	on large-scale data.},
}

repository: mreid/papersite

repo_name: mreid/papersite

gha_language: TeX


text:
@manual{R-ezpickr,
  title  = {ezpickr: Easy Data Import Using GUI File Picker and Seamless
Communication Between an Excel and R},
  author = {JooYoung Seo and Soyoung Choi},
  year   = {2019},
  note   = {Over 18K download},
  url    = {https://CRAN.R-project.org/package=ezpickr}
}
@manual{R-mboxr,
  title  = {mboxr: Reading, Extracting, and Converting an Mbox File into a Tibble},
  author = {JooYoung Seo and Soyoung Choi},
  year   = {2019},
  note   = {Over 14K download},
  url    = {https://CRAN.R-project.org/package=mboxr}
}
@manual{R-youtubecaption,
  title  = {youtubecaption: Downloading YouTube Subtitle Transcription in a Tidy Tibble Data
Frame},
  author = {JooYoung Seo and Soyoung Choi},
  year   = {2020},
  note   = {Over 8923 download},
  url    = {https://CRAN.R-project.org/package=youtubecaption}
}

repository: HugoBothaMD/academic-kickstart

repo_name: HugoBothaMD/academic-kickstart