cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/8e02-8v79
title: Cyber-security Modbus ICS dataset

description: This dataset was generated on a small-scale process automation scenario using MODBUS/TCP equipment, for research on the application of ML techniques to cybersecurity in Industrial Control Systems. The testbed emulates a CPS process controlled by a SCADA system using the MODBUS/TCP protocol. It consists of a liquid pump simulated by an electric motor controlled by a variable frequency drive (allowing for multiple rotor speeds), which in its turn controlled by a Programmable Logic Controller (PLC). The motor speed is determined by a set of predefined liquid temperature thresholds, whose measurement is provided by a MODBUS Remote Terminal Unit (RTU) device providing a temperature gauge, which is simulated by a potentiometer connected to an Arduino. The PLC communicates horizontally with the RTU, providing insightful knowledge of how this type of communications may have an effect on the overall system. The PLC also communicates with the Human-Machine Interface (HMI) controlling the system. The testbed is depicted in the image hereby included.The provided sample corresponds to roughly one third of the total available captured traces.The full network trace data sets are available at:https://github.com/tjcruz-dei/ICS_PCAPS/releases/tag/MODBUSTCP#1Citation RequestFrazão, I. and Pedro Henriques Abreu and Tiago Cruz and Araújo, H. and Simões, P. , "Denial of Service Attacks: Detecting the frailties of machine learning algorithms in the Classication Process", in 13th International Conference on Critical Information Infrastructures Security (CRITIS 2018), ed. Springer, Kaunas, Lithuania, September 24-26, 2018, Springer series on Security and Cryptology , 2018. DOI: 10.1007/978-3-030-05849-4_19
DOI: 10.5286/isis.e.rb1820387-3
title: 1820387
DOI: 10.5286/isis.e.rb1910128-1
title: 1910128
DOI: 10.5286/isis.e.rb1910128-2
title: 1910128
DOI: 10.5286/isis.e.rb1910635-2
title: 1910635
DOI: 10.14272/baibhohksyvvcm-uhfffaoysa-n.1
title: C17H16O
title: InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2

description: This dataset contains experimental data from a sample (tricyclo[8.2.2.2^{4,7}]hexadeca-1(13),4,6,10(14),11,15-hexaene-5-carbaldehyde, InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2).
      The datasets from the following experiments are available:
      
      1H NMR
      
      13C NMR
      
      Mass
      
      IR
DOI: 10.14272/baibhohksyvvcm-uhfffaoysa-n/13cnmr
title: 13C NMR (tricyclo[8.2.2.2^{4,7}]hexadeca-1(13),4,6,10(14),11,15-hexaene-5-carbaldehyde, InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2)

description: dataset for 13C NMR
      
      13C NMR (75 MHz, CDCl3, ppm) δ =  191.9 (+, CH, C=O), 143.2 (Cquat., CAr), 140.7 (Cquat., CAr), 139.5 (Cquat., CAr) 139.5 (Cquat., CAr), 138.1 (+, CH, CAr), 136.6 (Cquat., CAr), 136.4 (+, CH, CAr), 136.1 (+, CH, CAr), 133.3 (+, CH, CAr), 132.9 (+, CH, CAr), 132.4 (+, CH, CAr), 132.2 (+, CH, CAr), 35.3 (‒, CH2), 35.2 (–, CH2), 35.0 (–, CH2), 33.5 (–, CH2).
DOI: 10.14272/baibhohksyvvcm-uhfffaoysa-n/1hnmr
title: 1H NMR (tricyclo[8.2.2.2^{4,7}]hexadeca-1(13),4,6,10(14),11,15-hexaene-5-carbaldehyde, InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2)

description: dataset for 1H NMR
      
      1H NMR (300 MHz, CDCl3, ppm) δ =  9.95 (s, 1H, O=CH), 7.02 (d, J = 2.0 Hz, 1H, HAr), 6.73 (dd, J = 7.8 Hz, J = 2.0 Hz, 1H, HAr), 6.59 (d, J = 7.8 Hz, 1H, HAr), 6.56 (dd, J = 7.9 Hz, J = 1.9 Hz, 1H, HAr),  6.50 (dd, J = 7.9 Hz, 1.9 Hz, 1H, HAr), 6.43 (dd, J = 7.9 Hz, 1.9 Hz, 1H, HAr), 6.38 (dd, J = 7.9 Hz, J = 1.9 Hz, 1H, HAr), 4.17-4.05 (m, 1H, HPC ), 3.31–2.90 (m, 7H, HPC).
DOI: 10.14272/baibhohksyvvcm-uhfffaoysa-n/ir
title: IR (tricyclo[8.2.2.2^{4,7}]hexadeca-1(13),4,6,10(14),11,15-hexaene-5-carbaldehyde, InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2)

description: dataset for IR
      
      IR (ATR): (cm-1) = 2924 (w), 2850 (w), 1676 (m), 1589 (w), 1554 (w), 1497 (w), 1435 (w), 1410 (w), 1282 (w), 1226 (w), 1181 (w), 1141 (w), 1102 (w), 942 (w), 904 (w), 875 (w), 795 (w), 772 (w), 719 (m), 635 (m), 574 (m), 515 (m) 495 (w), 385 (w).
DOI: 10.14272/baibhohksyvvcm-uhfffaoysa-n/mass
title: Mass (tricyclo[8.2.2.2^{4,7}]hexadeca-1(13),4,6,10(14),11,15-hexaene-5-carbaldehyde, InChI=1S/C17H16O/c18-12-17-11-15-6-5-13-1-3-14(4-2-13)7-9-16(17)10-8-15/h1-4,8,10-12H,5-7,9H2)

description: dataset for Mass
      
      MS (70 eV, EI), m/z (%): 236 (35) [M]+, 132 (12) [M–C8H8]+, 104 (100) [C8H8]+; HRMS (C17H16O) Calcd: 236.1201; Found: 236.1203.
DOI: 10.21227/z0mg-8m78
title: Cyber-security Modbus ICS dataset

description: This dataset was generated on a small-scale process automation scenario using MODBUS/TCP equipment, for research on the application of ML techniques to cybersecurity in Industrial Control Systems. The testbed emulates a CPS process controlled by a SCADA system using the MODBUS/TCP protocol. It consists of a liquid pump simulated by an electric motor controlled by a variable frequency drive (allowing for multiple rotor speeds), which in its turn controlled by a Programmable Logic Controller (PLC). The motor speed is determined by a set of predefined liquid temperature thresholds, whose measurement is provided by a MODBUS Remote Terminal Unit (RTU) device providing a temperature gauge, which is simulated by a potentiometer connected to an Arduino. The PLC communicates horizontally with the RTU, providing insightful knowledge of how this type of communications may have an effect on the overall system. The PLC also communicates with the Human-Machine Interface (HMI) controlling the system. The testbed is depicted in the image hereby included.The provided sample corresponds to roughly one third of the total available captured traces.The full network trace data sets are available at:https://github.com/tjcruz-dei/ICS_PCAPS/releases/tag/MODBUSTCP#1Citation RequestFrazão, I. and Pedro Henriques Abreu and Tiago Cruz and Araújo, H. and Simões, P. , "Denial of Service Attacks: Detecting the frailties of machine learning algorithms in the Classication Process", in 13th International Conference on Critical Information Infrastructures Security (CRITIS 2018), ed. Springer, Kaunas, Lithuania, September 24-26, 2018, Springer series on Security and Cryptology , 2018. DOI: 10.1007/978-3-030-05849-4_19
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n.1
title: C26H28N4
title: InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3

description: This dataset contains experimental data from a sample (InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3).
      The datasets from the following experiments are available:
      
      IR
      
      1H NMR
      
      13C NMR
      
      Mass
      
      Mass
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n/13cnmr
title: 13C NMR (, InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3)

description: dataset for 13C NMR
      
      13C NMR (101 MHz, CDCl3, ppm) δ = 143.5 (CHAr), 142.9 (CAr), 140.4 (CAr), 139.6 (CAr), 139.2 (CAr), 137.9 (CAr), 137.1 (CAr), 135.1 (CHAr), 133.8 (CHAr), 133.8 (CHAr), 133.5 (CHAr), 133.5 (CHAr), 132.7 (CHAr), 132.5 (CHAr), 132.0 (CHAr), 128.7 (CHAr), 126.1 (CAr), 116.7 (CHAr), 57.0 (C), 35.6 (CH2), 35.5 (CH2), 35.4 (CH2), 33.8 (CH2), 29.9 (3 × CH3).
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n/1hnmr
title: 1H NMR (, InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3)

description: dataset for 1H NMR
      
      1H NMR (400 MHz, CDCl3, ppm) δ = 9.13 (d, J = 1.5 Hz, 1H, CHAr), 8.16 (dd, J = 4.6, 1.5 Hz, 1H, CHAr), 7.86 (d, J = 4.6 Hz, 1H, CHAr), 6.97 (d, J = 1.4 Hz, 1H, CHAr), 6.71 (s, 1H, CHAr), 6.65 – 6.53 (m, 2H, CHAr), 6.53 (s, 2H, CHAr), 3.33 – 2.99 (m, 6H, CH2), 2.96 – 2.81 (m, 2H, CH2), 0.77 (s, 9H, 3 × CH3).
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n/ir
title: IR (, InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3)

description: dataset for IR
      
      IR (ATR, ṽ) =  3351 (vw), 2952 (w), 2627 (w), 1685 (vw), 1594 (vw), 1523 (w), 1476 (w), 1419 (w), 1379 (w), 1362 (w), 1341 (w), 1285 (w), 1198 (w), 1012 (vw), 934 (vw), 903 (w), 863 (vw), 809 (w), 712 (w), 669 (w), 639 (w), 617 (w), 550 (vw), 512 (w), 449 (vw), 416 (w), 384 (vw) cm–1.
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n/mass
title: Mass (, InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3)

description: dataset for Mass
      
      FAB m/z (%): 398 (33), 397 (100), 396 (54), 340 (25).
DOI: 10.14272/xdvgamlxrdowhu-uhfffaoysa-n/mass.1
title: Mass (, InChI=1S/C26H28N4/c1-26(2,3)29-25-24(28-23-17-27-14-15-30(23)25)22-16-20-9-8-18-4-6-19(7-5-18)10-12-21(22)13-11-20/h4-7,11,13-17,29H,8-10,12H2,1-3H3)

description: dataset for Mass
      
      HRMS-FAB (m/z): [M + H]+ calcd. for C26H29N4, 397.2392; found, 397.2394.
DOI: 10.5281/zenodo.2554227
title: 0.3 ms of asymmetric GM1:POPC/POPC (24:76/97) bilayer

description: Lollipop-like GM1. Sixth 25 microseconds of total 300 microseconds.
DOI: 10.5281/zenodo.2554239
title: Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes

description: These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).
description: For a full listing of the 16 authors please see the title page of the manuscript.
DOI: 10.5281/zenodo.2554240
title: Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes

description: These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).
description: For a full listing of the 16 authors please see the title page of the manuscript.
DOI: 10.21251/d8a870b2-660b-4923-9da9-b532543a775d
title: Test 2
DOI: 10.5281/zenodo.2553872
title: Database of movement behavior and EEG in virtual audiovisual everyday-life environments for hearing aid research

description: This database contains movement behavior (head, eye, torso) and EEG signals of 21 young normal-hearing (11 male, 11 female, mean age 25 +/- 3.6 years) and 19 elderly normal-hearing subjects (9 male, 12 female, mean age 69 +/- 5.4 years) measured in virtual audiovisual listening environments in the laboratory. The virtual audiovisual environments that were used are: a living room, a lecture hall, a cafeteria, a street and a train station. The video and audio material for the environments is also available (see Related identifiers). The methods and an analysis of the movement behavior are described in Hendrikse et al. (2019). The supplementary materials to this paper that are published here include plots of the gaze trajectories of the subjects in all environments, plotted separately for the young and elderly subjects so that they can be compared, and histograms of the head-, eye- and torso-rotation for the environments that were not included in the paper.
description: Funding: DFG SFB 1330 project B1 and EU's H2020 research and innovation programme under the MSCA GA 675324.
description: {"references": ["Hendrikse et al. (n.d.). \"Movement and gaze behavior in virtual audiovisual everyday-life listening environments\" submitted to Trends in Hearing."]}
DOI: 10.18739/a2930nv02
title: Thaw depth at Toolik Field Station, Alaska, 2017-2018
DOI: 10.5281/zenodo.2553875
title: District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"

description: Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.
DOI: 10.18739/a21n7xm5b
title: Year-round soil moisture and temperature, Toolik Field Station, Alaska, 2017-2018
DOI: 10.18739/a2599z17n
title: Year-round soil pore space carbon dioxide and temperature at Toolik Field Station, Alaska, 2017-2018
DOI: 10.18739/a2ww77017
title: EAGER: Quantifying the Sources of Arctic Tundra-Respired CO2 Year-Round via Continuous in Situ Sampling of 14CO2, Alaska, 2017-2018
DOI: 10.5886/nkzfxc
title: The bees of British Columbia (Hymenoptera: Apoidea, Apiformes)

description: Dataset contains a checklist of bee species known to occur in the Canadian province of British Columbia based on literature records and specimens.
DOI: 10.17632/8yjv7b3hgr.1
title: Data for: Face analysis through semantic face segmentation

description: The FASSEG repository is composed by three datasets containing face images useful for training and testing automatic methods for the task of face segmentation. Two datasets, namely frontal01 and frontal02, are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelled ground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. The third dataset, namely Multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth mask data are labelled on six classes (mouth, nose, eyes, hair, skin, and background). All images of each dataset come organized in two subfolders - train and test – for enabling comparisons between different segmentation methods on the same datasets.
DOI: 10.5281/zenodo.2554214
title: Binary black-hole simulation SXS:BBH:1392

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2554213
title: Binary black-hole simulation SXS:BBH:1392

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.17632/3y975x6frh.1
title: Supporting Information 2

description: Supporting Information 2 : Quantifying net water consumption of Norwegian hydropower reservoirs and related aquatic biodiversity impacts in Life Cycle Assessment Sheet-Explanation NWC: Net water consumption values and calculations CF: Characterization Factors quantifying the aquatic biodiversity impacts of water consumption RWE: Hyrdopower reservoir specific evaporation values and calculations
DOI: 10.5523/bris.23clppyxx8fj92uf97zr2u41vc
title: Data from BRACE pilot: axonal proteins (01-2019)
title: AMRC open research manuscript

description: Data deposit for AMRC open research manuscript submission.
Raw data files contained:
Genotyping (obtained with StepOnePlus system and software v2.2.2)
QPCR (obtained with StepOnePlus system and software v2.2.2)
Dot blot (obtained with Biorad ChemiDoc Imager)
Western blot
ELISA (obtained with Optima plate reader)
IHC counts (SWDBB case data)
DOI: 10.17632/j3bc8x36pn.1
title: Supporting Information 1

description: Supporting Information 1 : Quantifying net water consumption of Norwegian hydropower reservoirs and related aquatic biodiversity impacts in Life Cycle Assessment
DOI: 10.5281/zenodo.2554320
title: Ocean model fields shown in paper titled "E3SMv0-HiLAT: A Modifed Climate System Model Targeted for the Study of High Latitudes"

description: These files contain ocean model climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).
description: For a full listing of the 16 authors please see the title page of the manuscript.
DOI: 10.5281/zenodo.2554321
title: Ocean model fields shown in paper titled "E3SMv0-HiLAT: A Modifed Climate System Model Targeted for the Study of High Latitudes"

description: These files contain ocean model climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).
description: For a full listing of the 16 authors please see the title page of the manuscript.
DOI: 10.5281/zenodo.2554538
title: Binary black-hole simulation SXS:BBH:1398

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.2554539
title: Binary black-hole simulation SXS:BBH:1398

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.17632/8ps5z9m6fw.1
title: Data for: Electrospun nanofiber-based niflumic acid capsules with superior physicochemical properties
DOI: 10.17632/8yjv7b3hgr.2
title: Data for: Face analysis through semantic face segmentation

description: The FASSEG repository is composed by three datasets containing face images useful for training and testing automatic methods for the task of face segmentation. Two datasets, namely frontal01 and frontal02, are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelled ground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. The third dataset, namely Multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth mask data are labelled on six classes (mouth, nose, eyes, hair, skin, and background). All images of each dataset come organized in two subfolders - train and test – for enabling comparisons between different segmentation methods on the same datasets.
DOI: 10.17632/kmvnkfhf3c.1
title: Data for: Modeling Solar Chimney for Geometry Optimization

description: Optimization Data
DOI: 10.5281/zenodo.2554115
title: Keyword frequency in popular tech media

description: <strong>Keywords trending in the tech media (01.2016-03.2019)</strong>

<strong>Sources</strong>


	Gigaom 0.5%
	Euractiv 0.9%
	The Conversation 1.3%
	Politico Europe 1.3%
	IEEE Spectrum 1.8%
	Techforge 4.3%
	Fastcompany 4.5%
	The Guardian (Tech) 9.2%
	Arstechnica 10.0%
	Reuters 11%
	Gizmodo 17.5%
	ZDNet 18.3%
	The Register 19.5%


<strong>Methodology</strong>


	Frequency of appearances for all unigrams and bigrams in the texts
	Frequency: number of appearances of every term divided by the number of published articles (for every month and source)
	This measure reveals how many times an expression has been mentioned on average per article
	Several media sources: a representative index is calculated with weighted average
	Average monthly change in the analised term's frequency is calculated by OLS regressions
	The dependent variable of the estimation is the frequency index, while the number of months since the beginning of the analysed period (January 2016) is the independent variable
	The regression coefficient (referred to as coef) shows by how much on average the analysed expression’s frequency changed with every observed month (marginal change of the frequency), revealing which keywords had the biggest monthly growth


<strong>Files</strong>


	unigrams: coefs_1weighted_site.csv
	bigrams: coefs_2weighted_site.csv
DOI: 10.5281/zenodo.2554116
title: Keyword frequency in popular tech media

description: <strong>Keywords trending in the tech media</strong>

<strong>Sources</strong>


	Gigaom 0.5%
	The conversation 1%
	IEEE Spectrum 1.6%
	Techforge 3.8%
	Fastcompany 4.2%
	The Guardian (Tech) 8.3%
	Arstechnica 8.8%
	Reuters 9.4%
	Venturebeat 14.4%
	ZDNet 15%
	Gizmodo 15.4%
	The Register 17.5%


<strong>Methodology</strong>


	Frequency of appearances for all unigrams and bigrams in the texts
	Frequency: number of appearances of every term divided by the number of published articles (for every month and source)
	This measure reveals how many times an expression has been mentioned on average per article
	Several media sources: a representative index is calculated with weighted average
	Average monthly change in the analised term's frequency is calculated by OLS regressions
	The dependent variable of the estimation is the frequency index, while the number of months since the beginning of the analysed period (January 2016) is the independent variable
	The regression coefficient (referred to as coef) shows by how much on average the analysed expression’s frequency changed with every observed month (marginal change of the frequency), revealing which keywords had the biggest monthly growth


<strong>Files</strong>


	unigrams: coefs_1weighted_site.csv
	bigrams: coefs_2weighted_site.csv
DOI: 10.14272/cokmbpoibwkrdb-mtmgmadnsa-n.1
title: C30H46N4O5
title: InChI=1S/C30H46N4O5/c1-7-9-11-22-26(35)32-23(18-20-13-15-21(16-14-20)39-30(4,5)6)27(36)33-25(19(3)8-2)29(38)34-17-10-12-24(34)28(37)31-22/h13-16,19,22-25H,7-12,17-18H2,1-6H3,(H,31,37)(H,32,35)(H,33,36)/t19?,22-,23-,24+,25-/m0/s1

description: This dataset contains experimental data from a sample (InChI=1S/C30H46N4O5/c1-7-9-11-22-26(35)32-23(18-20-13-15-21(16-14-20)39-30(4,5)6)27(36)33-25(19(3)8-2)29(38)34-17-10-12-24(34)28(37)31-22/h13-16,19,22-25H,7-12,17-18H2,1-6H3,(H,31,37)(H,32,35)(H,33,36)/t19?,22-,23-,24+,25-/m0/s1).
      The datasets from the following experiments are available:
      
      1H NMR
      
      13C NMR
      
      Mass
DOI: 10.14272/cokmbpoibwkrdb-mtmgmadnsa-n/13cnmr
title: 13C NMR (, InChI=1S/C30H46N4O5/c1-7-9-11-22-26(35)32-23(18-20-13-15-21(16-14-20)39-30(4,5)6)27(36)33-25(19(3)8-2)29(38)34-17-10-12-24(34)28(37)31-22/h13-16,19,22-25H,7-12,17-18H2,1-6H3,(H,31,37)(H,32,35)(H,33,36)/t19?,22-,23-,24+,25-/m0/s1)

description: dataset for 13C NMR
      
      13C NMR (100 MHz, chloroform-d1) δ = 175.1 (Cq, CONH), 174.4 (Cq, CONH), 173.3 (Cq, CONH), 171.4 (Cq, CONH), 154.0 (Cq, tyr-CHCO), 131.7 (Cq, tyr-Car), 129.2 (+, 2 × tyr-CH), 124.1 (+, 2 × tyr-CH), 78.3 (Cq, tyr-OC(CH3)3), 62.9 (+, tyr-CαH), 57.7 (+, pro-CαH), 57.7 (+, ile-CαH), 54.1 (+, nle-CαH), 46.8 (–, pro-CδH2), 34.1 (–, tyr-CH2), 33.6 (+, ile-CH), 28.7 (–, nle-CβH2), 28.6 (+, 2 × tyr-CH3), 27.5 (–, ile-CH2), 24.8 (–, pro-CβH2), 24.7 (–, pro-CγH2), 25.6 (–, nle-CγH2), 22.2 (–, nle-CδH2), 15.6 (+, ile-CH3), 13.8 (+, nle-CH3), 10.5 (+, ile-CH3) ppm.
DOI: 10.14272/cokmbpoibwkrdb-mtmgmadnsa-n/1hnmr
title: 1H NMR (, InChI=1S/C30H46N4O5/c1-7-9-11-22-26(35)32-23(18-20-13-15-21(16-14-20)39-30(4,5)6)27(36)33-25(19(3)8-2)29(38)34-17-10-12-24(34)28(37)31-22/h13-16,19,22-25H,7-12,17-18H2,1-6H3,(H,31,37)(H,32,35)(H,33,36)/t19?,22-,23-,24+,25-/m0/s1)

description: dataset for 1H NMR
      
      1H-NMR (chloroform-d1): δ = 7.44 (d, J = 10.7 Hz, 1H, ile-NαH), 7.16 (d, J = 10.2 Hz, 1H, nle-NαH), 7.10–7.03 (m, 2H, 2 × tyr-CH), 6.90–6.82 (m, 2H, 2 × tyr-CH), 6.68 (d, J = 6.0 Hz, 1H, tyr-NαH), 4.75 (dd, J = 7.9, 1.8 Hz, 1H, pro-CαH), 4.52 (t, J = 10.6 Hz, 1H, ile-CαH), 4.15 (q, J = 9.5 Hz, 1H, nle-CαH), 3.91–3.83 (m, 1H, pro-CδHH), 3.75 (dt, J = 10.1, 6.4 Hz, 1H, tyr-CαH), 3.65 (dd, J = 13.5, 10.6 Hz, 1H, tyr-CHH), 3.54–3.45 (m, 1H, pro-CδHH), 3.29 (dd, J = 13.7, 6.6 Hz, 1H, tyr-CHH), 2.36–2.29 (m, 1H, pro-CγHH), 2.26–2.16 (m, 1H, pro-CγHH), 2.09–2.00 (m, 1H, ile-CH), 1.96–1.87 (m, 1H, pro-CβHH), 1.86–1.75 (m, 2H, nle-CβHH, nle-CγHH), 1.65–1.58 (m, 1H, pro-CβHH), 1.57–1.49 (m, 1H, nle-CβHH), 1.37–1.22 (m, 11H, nle-CδH2, 3 × tyr-CH3), 1.20–1.13 (m, 3H, ile-CH2, nle-CγHH), 0.93–0.84 (m, 9H, 2 × ile-CH3, nle-CH3) ppm.