cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8028643
title: Methoxycyclization of 1,5-Enynes by Coinage Metal Catalysts: Is Gold Always Superior?

description: Au(I) catalysts promote the intramolecular cycloisomerization of 1,<em>n</em>-enynes, as well as facilitate reactions with alcohols, characteristics that have led to their wide-spread in the synthesis of complex natural products. Literature reports of Cu and Ag species catalyzing the same or similar reactions are much less common. Here, we utilize molecular volcano plots to assess the activity of 45 coinage metal phosphine/phosphite catalysts possessing a variety of stereoelectronic properties for the methoxycyclization reaction of a prototypical 1,5-enyne. Our analysis reveals that the intrinsic properties of gold allow it to be coupled with a wide range of ligands, all of which exhibit high catalytic activity. On the other hand, the coupling of silver and copper with more traditional phosphines, such as triphenylphosphine, leads to catalysts with diminished catalytic ability. The activity of Ag and Cu species, however, can be enhanced by appending ligands with strong π-accepting character, which shifts these species closer to the volcano peak.
DOI: 10.5281/zenodo.8028644
title: Methoxycyclization of 1,5-Enynes by Coinage Metal Catalysts: Is Gold Always Superior?

description: Au(I) catalysts promote the intramolecular cycloisomerization of 1,<em>n</em>-enynes, as well as facilitate reactions with alcohols, characteristics that have led to their wide-spread in the synthesis of complex natural products. Literature reports of Cu and Ag species catalyzing the same or similar reactions are much less common. Here, we utilize molecular volcano plots to assess the activity of 45 coinage metal phosphine/phosphite catalysts possessing a variety of stereoelectronic properties for the methoxycyclization reaction of a prototypical 1,5-enyne. Our analysis reveals that the intrinsic properties of gold allow it to be coupled with a wide range of ligands, all of which exhibit high catalytic activity. On the other hand, the coupling of silver and copper with more traditional phosphines, such as triphenylphosphine, leads to catalysts with diminished catalytic ability. The activity of Ag and Cu species, however, can be enhanced by appending ligands with strong π-accepting character, which shifts these species closer to the volcano peak.
DOI: 10.57745/3zax2w
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DOI: 10.57745/zd86iy
title: WHITEREF_2020-09-10_092.hdr
DOI: 10.57745/ca1xyo
title: WHITEREF_2020-09-10_092.raw
DOI: 10.57745/dzoect
title: 2020-09-10_092.xml
DOI: 10.57745/bpduc9
title: REFLECTANCE_2020-09-10_092.dat
DOI: 10.57745/4f8fjl
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DOI: 10.57745/ilgvoz
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DOI: 10.57745/hrpiq5
title: RGBBACKGROUND_2020-09-10_092.png
DOI: 10.57745/w9lacp
title: RGBSCENE_2020-09-10_092.png
DOI: 10.57745/tqjueu
title: RGBVIEWFINDER_2020-09-10_092.png
DOI: 10.57745/xk05ho
title: 2020-09-10_093.png
DOI: 10.57745/aq4awz
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DOI: 10.57745/7varso
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DOI: 10.57745/5fsvux
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DOI: 10.57745/ippdbe
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DOI: 10.57745/rsl3wg
title: RGBSCENE_2020-09-10_093.png
DOI: 10.57745/9wu6fu
title: RGBVIEWFINDER_2020-09-10_093.png
DOI: 10.57745/mmtdfr
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DOI: 10.57745/q7tb0k
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DOI: 10.57745/ttwaae
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DOI: 10.57745/yuxqff
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DOI: 10.57745/vzqbfs
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DOI: 10.57745/pl1em1
title: RGBBACKGROUND_2020-09-10_094.png
DOI: 10.57745/luflwy
title: RGBSCENE_2020-09-10_094.png
DOI: 10.57745/herzkc
title: RGBVIEWFINDER_2020-09-10_094.png
DOI: 10.57745/9qsp0g
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DOI: 10.57745/w3ejxf
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DOI: 10.57745/dbd8hi
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DOI: 10.57745/kugrra
title: DARKREF_2020-09-10_095.raw
DOI: 10.57745/c66osg
title: WHITEREF_2020-09-10_095.hdr
DOI: 10.57745/lqr85r
title: WHITEREF_2020-09-10_095.raw
DOI: 10.57745/qm6kfe
title: 2020-09-10_095.xml
DOI: 10.57745/vgvktk
title: REFLECTANCE_2020-09-10_095.dat
DOI: 10.57745/dzx6ng
title: REFLECTANCE_2020-09-10_095.hdr
DOI: 10.57745/dmmufu
title: REFLECTANCE_2020-09-10_095.png
DOI: 10.57745/ct0upf
title: RGBBACKGROUND_2020-09-10_095.png
DOI: 10.57745/3fph78
title: RGBSCENE_2020-09-10_095.png
DOI: 10.57745/91o5k4
title: RGBVIEWFINDER_2020-09-10_095.png
DOI: 10.57745/yecsoh
title: 2020-09-10_096.png
DOI: 10.57745/ptxesr
title: 2020-09-10_096.hdr
DOI: 10.57745/1ktice
title: 2020-09-10_096.raw
DOI: 10.57745/0xiikq
title: DARKREF_2020-09-10_096.hdr
DOI: 10.57745/dguoqt
title: DARKREF_2020-09-10_096.raw
DOI: 10.57745/iozkav
title: WHITEREF_2020-09-10_096.hdr
DOI: 10.57745/z7loe8
title: WHITEREF_2020-09-10_096.raw
DOI: 10.57745/zhfdzl
title: 2020-09-10_096.xml
DOI: 10.57745/rscjzw
title: REFLECTANCE_2020-09-10_096.dat
DOI: 10.57745/sfayfj
title: REFLECTANCE_2020-09-10_096.hdr
DOI: 10.57745/mrzfny
title: REFLECTANCE_2020-09-10_096.png
DOI: 10.57745/o8xtoj
title: RGBBACKGROUND_2020-09-10_096.png
DOI: 10.57745/xbzcjp
title: RGBSCENE_2020-09-10_096.png
DOI: 10.57745/esawnn
title: RGBVIEWFINDER_2020-09-10_096.png
DOI: 10.57745/twuxuk
title: 2020-09-10_097.png
DOI: 10.57745/fuxqft
title: 2020-09-10_097.hdr
DOI: 10.5281/zenodo.8027570
title: Mapping Catalyst–Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions

description: Selectivity and activity maps can be used to predict and rationalize reaction outcomes based on catalyst and solvent choice. Here, there utility is demonstrated by examining competing carboamination/cyclopropanation reactions catalyzed by Group 9 catalysts featuring cyclopentadienyl ligands, which reveals an interesting energetic interplay involving the role of the solvent and of the catalyst.
description: Archived version of the available Supporting Information.
DOI: 10.5281/zenodo.8027571
title: Mapping Catalyst–Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions

description: Selectivity and activity maps can be used to predict and rationalize reaction outcomes based on catalyst and solvent choice. Here, there utility is demonstrated by examining competing carboamination/cyclopropanation reactions catalyzed by Group 9 catalysts featuring cyclopentadienyl ligands, which reveals an interesting energetic interplay involving the role of the solvent and of the catalyst.
description: Archived version of the available Supporting Information.
DOI: 10.57745/jfv2og
title: 2020-09-10_097.raw
DOI: 10.57745/ksb31y
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DOI: 10.57745/55s6b0
title: DARKREF_2020-09-10_097.raw
DOI: 10.57745/owtpl3
title: WHITEREF_2020-09-10_097.hdr
DOI: 10.57745/xgt61y
title: WHITEREF_2020-09-10_097.raw
DOI: 10.57745/r8qdf8
title: 2020-09-10_097.xml
DOI: 10.57745/kvy2xi
title: REFLECTANCE_2020-09-10_097.dat
DOI: 10.57745/osu5ag
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DOI: 10.57745/meiopi
title: REFLECTANCE_2020-09-10_097.png
DOI: 10.57745/jabb0e
title: RGBBACKGROUND_2020-09-10_097.png
DOI: 10.57745/le0moj
title: RGBSCENE_2020-09-10_097.png
DOI: 10.57745/ckqgwb
title: RGBVIEWFINDER_2020-09-10_097.png
DOI: 10.57745/z1k8vu
title: 2020-09-10_098.png
DOI: 10.57745/twbdrd
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DOI: 10.57745/hm32n2
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DOI: 10.57745/hnc5a3
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DOI: 10.57745/f0bgpp
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DOI: 10.57745/dhdajv
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DOI: 10.57745/clwe9p
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DOI: 10.57745/yi3r7t
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DOI: 10.57745/vhohd6
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DOI: 10.57745/2mc37z
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DOI: 10.57745/vmbfdq
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DOI: 10.57745/nxnmzg
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DOI: 10.57745/tjponr
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DOI: 10.57745/cx11zh
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DOI: 10.57745/8siedj
title: 2020-09-10_099.xml
DOI: 10.57745/mjpc3v
title: REFLECTANCE_2020-09-10_099.dat
DOI: 10.57745/zdk3pg
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DOI: 10.57745/vs1ksr
title: REFLECTANCE_2020-09-10_099.png
DOI: 10.57745/eicyec
title: RGBBACKGROUND_2020-09-10_099.png
DOI: 10.57745/0on5ys
title: RGBSCENE_2020-09-10_099.png
DOI: 10.57745/affuom
title: RGBVIEWFINDER_2020-09-10_099.png
DOI: 10.57745/lwhz5a
title: 2020-09-10_101.png
DOI: 10.57745/91vsvf
title: 2020-09-10_101.hdr
DOI: 10.57745/aukzo2
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DOI: 10.57745/yq2vqk
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DOI: 10.57745/rueyfn
title: DARKREF_2020-09-10_101.raw
DOI: 10.57745/kb2ail
title: WHITEREF_2020-09-10_101.hdr
DOI: 10.57745/uvpy7l
title: WHITEREF_2020-09-10_101.raw
DOI: 10.57745/0kms6t
title: 2020-09-10_101.xml
DOI: 10.57745/jxh6d9
title: REFLECTANCE_2020-09-10_101.dat
DOI: 10.57745/lyj5sa
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DOI: 10.57745/vliquq
title: REFLECTANCE_2020-09-10_101.png
DOI: 10.57745/5e1t4r
title: RGBBACKGROUND_2020-09-10_101.png
DOI: 10.57745/x00mmf
title: RGBSCENE_2020-09-10_101.png
DOI: 10.57745/uzyucr
title: RGBVIEWFINDER_2020-09-10_101.png
DOI: 10.57745/x8kchm
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DOI: 10.57745/liwj2l
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DOI: 10.57745/u91dfy
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DOI: 10.57745/xrcbhn
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DOI: 10.57745/m4aetg
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DOI: 10.57745/zsdbsw
title: 2020-09-10_102.xml
DOI: 10.57745/fb8mqe
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DOI: 10.57745/rfxc7o
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DOI: 10.57745/7zzhui
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DOI: 10.57745/zgzxwt
title: RGBSCENE_2020-09-10_102.png
DOI: 10.57745/dnuine
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DOI: 10.57745/4yjiio
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DOI: 10.57745/ym5hru
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DOI: 10.57745/uaprzx
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DOI: 10.57745/vmcgn4
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DOI: 10.57745/ce2tpl
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DOI: 10.57745/cq0uha
title: RGBSCENE_2020-09-10_103.png
DOI: 10.57745/tmoebu
title: RGBVIEWFINDER_2020-09-10_103.png
DOI: 10.57745/qvy1wg
title: 2020-09-10_104.png
DOI: 10.57745/ivs0de
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DOI: 10.57745/uk5wih
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DOI: 10.57745/4vc4z8
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DOI: 10.57745/ustsoy
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DOI: 10.57745/tdl9bu
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DOI: 10.57745/0kbx4y
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DOI: 10.57745/7so5w5
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DOI: 10.57745/wjijvo
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DOI: 10.57745/iw5z43
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DOI: 10.57745/rit2by
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DOI: 10.57745/y2dpp9
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DOI: 10.57745/jtlhmh
title: RGBSCENE_2020-09-10_104.png
DOI: 10.57745/inq28v
title: RGBVIEWFINDER_2020-09-10_104.png
DOI: 10.57745/1vdtuv
title: 2020-09-10_105.png
DOI: 10.57745/y1qxgr
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DOI: 10.57745/gh1t0z
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DOI: 10.57745/j6wuj4
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DOI: 10.57745/pjswfj
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DOI: 10.57745/l6nfbb
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DOI: 10.57745/vivhnh
title: 2020-09-10_105.xml
DOI: 10.57745/bil9w3
title: REFLECTANCE_2020-09-10_105.dat
DOI: 10.57745/i9gzks
title: REFLECTANCE_2020-09-10_105.hdr
DOI: 10.57745/5lx40f
title: REFLECTANCE_2020-09-10_105.png
DOI: 10.57745/beq9ek
title: RGBBACKGROUND_2020-09-10_105.png
DOI: 10.57745/d9tblx
title: RGBSCENE_2020-09-10_105.png
DOI: 10.57745/qfugd4
title: RGBVIEWFINDER_2020-09-10_105.png
DOI: 10.5281/zenodo.7839834
title: Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms - SLR Dataset

description: Dataset of the publications collected at various stages of the Structured Literature Review titled "Design of Blockchain-based Applications using Model-Driven Engineering and Low-Code / No-Code Platforms" published in Software and Systems Modeling: The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, low-code and no-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we extend a review of academic approaches from the discipline of model-driven engineering as well as industrial low-code and no-code development platforms for blockchains. This includes a content-based, computational analysis of relevant academic papers and the derivation of major topics. In addition, the topics were manually evaluated and refined. Based on these analyses we discuss the spectrum of approaches in this field and derive opportunities for further research.
description: This work was supported by the Swiss National Science Foundation project Domain-Specific Conceptual Modeling for Distributed Ledger Technologies [196889].
DOI: 10.57745/sues50
title: 2020-09-10_106.png
DOI: 10.5066/f79s1p57
title: Surface area of solar arrays in the conterminous United States

description: The U.S. Geological Survey (USGS) Fort Collins Science Center created a national data set of the footprint area of solar arrays for the Bureau of Land Managements National Operations Center. We identified potential solar facility locations for the conterminous U.S. using the U.S. Energy Information Administration (EIA) utility-scale facilities data from 2015. The footprint area of each solar array was digitized on-screen using true color aerial imagery from the most recently available National Agriculture Imagery Program (NAIP) for each state (NAIP 2013-2015). We used supplementary ESRI basemap imagery accessed via ArcGIS to verify locations of arrays observed in NAIP imagery. Arrays separated by more than 30 meters were delineated as separate polygons. Access roads and other adjacent infrastructure were not included in the array footprint. Attribution for each array was derived from the EIA data and augmented with the date of aerial imagery used for delineation, the area of each array, and ground surface characteristics (artificial impervious or non-impervious). As of 2015, solar arrays covered a total of 191.57 square kilometers, only 2.8 percent of which was located on impervious surfaces. There were 176 utility-scale solar facilities (319 arrays) on impervious surfaces and 564 facilities (888 arrays) on non-impervious surfaces.
DOI: 10.5066/f7ft8jxp
title: USGS Gap Analysis Project Additional Data: Aspect

description: Aspect is the compass direction toward which a slope faces, measured in degrees from North in a clockwise direction from 0 to 360. Aspect was generated using ArcGIS Spatial Analyst, with the USGS National Elevation Dataset as input. For quality information regarding the National Elevation Dataset, see http://nationalmap.gov/elevation.html. These data cover the entire continental U.S. and are a continuous data layer. These raster data have a 30 m x 30 m cell resolution. The USGS Gap Analysis Project (GAP) used the best information available to create the aspect data; however GAP seeks to improve and update these data as new information becomes available.
DOI: 10.5066/p9qv9bpn
title: Reflectance measurements for eight samples submitted to the USGS Organic Petrology Laboratory in Reston

description: This data release contains reflectance measurements for eight samples submitted to the USGS Organic Petrology Laboratory in Reston. Samples include two artificial rock samples made from cement with organic matter added (samples 001-002), a coal and shale sample (samples 003-004, respectively), and hydrous pyrolysis residues for samples 001-004. The hydrous pyrolysis experiments were conducted at 330°C for 72 hours at the United States Geological Survey's (USGS) Denver Federal Center. Samples were mounted into 1-inch circular briquettes in USGS-Reston according to ASTM D2797 for organic petrographic analysis. Cement plugs (samples 001-002, and pyrolysis residues 005-006) were crushed to approximately 1 mm top-size whereas rock powders (samples 003-004, and pyrolysis residues 007-008) were prepared as-received (chips and powders of varying size). Samples were mounted in a thermoplastic medium using a heat-setting hydraulic press. Examination surfaces were ground and mechanically polished to a 0.05 µm finish with a silica colloid suspension. Reflectance measurements on the coal sample and the coal pyrolysis residue followed ASTM D2798 whereas reflectance measurements on cement plugs and shales followed ASTM D7708. A Leica DM4000 microscope equipped with LED illumination and monochrome camera detection was used for reflectance analysis with the computer program DISKUS-FOSSIL by Hilgers Technisches Buero. A YAG calibration standard (0.908% Ro) from Klein and Becker was used to calibrate the microscope for all reflectance measurements.
DOI: 10.57745/tyshar
title: 2020-09-10_106.hdr
DOI: 10.6084/m9.figshare.20291934.v2
title: Prevalence and Determinants of Obesity among Indonesian Diabetics

description: The present study aimed to determine the prevalence and determinants of obesity among diabetic individuals in Indonesia
DOI: 10.57745/oluwk5
title: 2020-09-10_106.raw
DOI: 10.57745/coamuk
title: DARKREF_2020-09-10_106.hdr
DOI: 10.57745/i5svvo
title: DARKREF_2020-09-10_106.raw
DOI: 10.57745/jjag1j
title: WHITEREF_2020-09-10_106.hdr
DOI: 10.57745/sb3tmw
title: WHITEREF_2020-09-10_106.raw
DOI: 10.57745/a8ynma
title: 2020-09-10_106.xml
DOI: 10.57745/hqbt3s
title: REFLECTANCE_2020-09-10_106.dat
DOI: 10.57745/m7fyhp
title: REFLECTANCE_2020-09-10_106.hdr
DOI: 10.57745/srteqw
title: REFLECTANCE_2020-09-10_106.png
DOI: 10.26302/sshade/experiment_soc_20181115_004
title: Fe K edge XAS HERFD (Kbeta1,3) and XES of synthetic iron(II) oxide FeO at ambient conditions
DOI: 10.5281/zenodo.7520022
title: Sulfitobacter_pontiacus_isolate_huxleyi1516

description: Genome assembly of the bacterial strain Sulfitobacter pontiacus which was isolated from the algal strain <em>Emiliania huxleyi</em> CCMP1516. Genome sequencing was performed using PacBio platform. The assembly of the genome was performed in the analysis software SMRTlink.
DOI: 10.57745/o6gkr6
title: RGBBACKGROUND_2020-09-10_106.png
DOI: 10.6084/m9.figshare.19253363
title: MAGs

description: 72 high and medium quality metagenome assembled genomes from metagenomic samples taken from within a Hawaiian lava tube.
DOI: 10.6084/m9.figshare.22696237
title: WGS Data

description: Whole genome shotgun (WGS) data for the Extreme Niche Partitioning and Microbial Dark Matter in a Mauna Loa Lava Tube.
DOI: 10.5281/zenodo.5776718
title: MPIC OMI Total Column Water Vapour (TCWV) Climate Data Record

description: The upload contains a long-term data set of 1°x 1° monthly mean total column water vapour (TCWV) retrieved in the visible "blue" spectral range from global measurements of the Ozone Monitoring Instrument (OMI). The TCWV data set covers the time range from January 2005 to December 2020.
DOI: 10.57745/1wiint
title: RGBSCENE_2020-09-10_106.png
DOI: 10.6084/m9.figshare.19253372.v1
title: 16S SSU rRNA Sample

description: Mauna Loa lava tube microbial communities, including the fastq files for 16 S SSU rRNA.
DOI: 10.57745/0agyxu
title: RGBVIEWFINDER_2020-09-10_106.png
DOI: 10.57745/ka9dfd
title: 2020-09-10_107.png
DOI: 10.57745/myxreb
title: 2020-09-10_107.hdr
DOI: 10.57745/xty2ei
title: 2020-09-10_107.raw
DOI: 10.57745/tlfgje
title: DARKREF_2020-09-10_107.hdr
DOI: 10.57745/j1xdhh
title: DARKREF_2020-09-10_107.raw
DOI: 10.57745/glkxfd
title: WHITEREF_2020-09-10_107.hdr
DOI: 10.57745/vlby8a
title: WHITEREF_2020-09-10_107.raw
DOI: 10.3886/icpsr03523
title: National Institute of Education (NIE) Special Tabulations and 1970 Census Fifth Count Data File
title: Archival Version

description: This data file contains school district-level data from the following two sources: (1) the National Institute of Education (NIE) Special Tabulations of 1970 Census data, retabulated to 1973-1974 school district boundaries, and (2) the 1970 Census of Population and Housing Fifth Count data. The data in this collection were extracted from the 1976-1977 Merged Federal File produced by AUI Policy Research. Since some districts on the 1976-1977 Merged Federal File had consolidated by 1978-1979, NIE Special Tabulations data for these districts were combined. The Census data file was created in three steps. First, a skeleton file was created, containing one record for each school district on the 1978-1979 Merged Federal File. Each record on the skeleton file contained those data items in the School District Identification segment on the Merged Federal File. Second, the NIE Special Tabulations data were merged by the Office of Education (OE) state code and Local Education Agency (LEA) code to the skeleton file. Finally, Census Fifth Count records were merged by OE state code and LEA code to the skeleton file.
description: Datasets: 
 
 DS1: Dataset
description: Data from the following Census Fifth Count tables were extracted for inclusion on this data file: (a) Table 1, Aggregate Income, (b) Table 2, Aggregate Family Income by Race, Family Status and Size, (c) Table 5, Nativity and Parentage, (d) Table 6, Mother Tongue, (e) Table 7, Count of Persons of Foreign Stock by Country of Origin, (f) Table 10, Count of Families by Family Type, Number of Children under 18 and Race, (g) Table 14, Population 25 Years Old and Over by Years of School Completed and Age, (h) Table 17, Labor Force by Race and Sex, (i) Table 18, Employed Population 16 Years Old and Older by Occupation, (j) Table 19, Employed Population 16 Years Old and Older by Industry, (k) Table 20, Income by Count of Families and Unrelated Individuals 14 Years Old and Over, (l) Table 30, Count of Occupied and Vacant Year-Round Housing Units, (m) Table 31, Count of Occupied Units by Race and Tenure, and (n) Table 40, Value of Owner-Occupied units.
description: All school districts in the United States.