cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5068013
title: Seismic noise recorded at Solfatara Volcano in April 2007

description: Seismic noise recorded during a seismic survey carried out at Solfatara Volcano in the period 2-6 April 2007. Five circular seismic arrays were deployed inside the crater; an other seismic station was installed on the eastern rim for a hardrock reference. Details on the experiment, as well as data description and station coordinates are reported in: Petrosino, S., Damiano, N., Cusano, P., Veneruso, M., Zaccarelli, L., Torello, V., & Del Pezzo, E. (2008). Seismic noise at Solfatara Volcano (Campi Flegrei, Italy): acquisition techniques and first results. <em>Quaderni di Geofisica</em>. Shallow crustal structure of Solfatara volcano, inferred from dataset analysis has been published in: Petrosino, S., Damiano, N., Cusano, P., Di Vito, M. A., de Vita, S., & Del Pezzo, E. (2012). Subsurface structure of the Solfatara volcano (Campi Flegrei caldera, Italy) as deduced from joint seismic‐noise array, volcanological and morphostructural analysis. <em>Geochemistry, Geophysics, Geosystems</em>, <em>13</em>(7).
description: {"references": ["Petrosino, S., Damiano, N., Cusano, P., Veneruso, M., Zaccarelli, L., Torello, V., & Del Pezzo, E. (2008). Seismic noise at Solfatara Volcano (Campi Flegrei, Italy): acquisition techniques and first results.\u00a0Quaderni di Geofisica.", "Petrosino, S., Damiano, N., Cusano, P., Di Vito, M. A., de Vita, S., & Del Pezzo, E. (2012). Subsurface structure of the Solfatara volcano (Campi Flegrei caldera, Italy) as deduced from joint seismic\u2010noise array, volcanological and morphostructural analysis.\u00a0Geochemistry, Geophysics, Geosystems,\u00a013(7)."]}
DOI: 10.5281/zenodo.5068024
title: A Comparative Analysis of Agile Teamwork Quality Models.

description: Supplementary material for article A Comparative Analysis of Agile Teamwork Quality Models.
DOI: 10.6084/m9.figshare.14905203
title: Table 9. Trend percentage of developing countries articles and total articles published

description: Trend percentage of developing countries articles and total articles published
DOI: 10.6084/m9.figshare.14905203.v1
title: Table 9. Trend percentage of developing countries articles and total articles published

description: Trend percentage of developing countries articles and total articles published
DOI: 10.6084/m9.figshare.14905212
title: Table 10. A-Star Journal publications on developing countries

description: A-Star Journal publications on developing countries
DOI: 10.6084/m9.figshare.14905212.v1
title: Table 10. A-Star Journal publications on developing countries

description: A-Star Journal publications on developing countries
DOI: 10.5281/zenodo.5068083
title: BD dual vs triple therapy asthma

description: BD paper dual vs triple therapy asthma
DOI: 10.5281/zenodo.5068082
title: BD dual vs triple therapy asthma

description: BD paper dual vs triple therapy asthma
DOI: 10.6084/m9.figshare.14905221
title: Table 11. A ranked Journal Publications on Developing Countries

description: A ranked Journal Publications on Developing Countries
DOI: 10.6084/m9.figshare.14905221.v1
title: Table 11. A ranked Journal Publications on Developing Countries

description: A ranked Journal Publications on Developing Countries
DOI: 10.6084/m9.figshare.14905239
title: Table 12. B-ranked Journal publications on developing countries

description: B-ranked Journal publications on developing countries
DOI: 10.6084/m9.figshare.14905239.v1
title: Table 12. B-ranked Journal publications on developing countries

description: B-ranked Journal publications on developing countries
DOI: 10.21227/06jf-r690
title: EEG signal, ECG signal

description: Many different signals are gained from the human body, they are called Biomedical signals or biosignals, they can be at cell level, organ level, or molecular level. electroencephalogram (EEG), which is electrical activity from the brain, electrical activity from the heart called electrocardiogram (ECG), electrical activity from the muscle sound signals known as electromyogram (EMG), the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, it can help them examine and predict and cure many diseases.
DOI: 10.6084/m9.figshare.14905311
title: Table 14. Total developing countries articles published per continent

description: Total developing countries articles published per continent <b></b>
DOI: 10.6084/m9.figshare.14905311.v1
title: Table 14. Total developing countries articles published per continent

description: Total developing countries articles published per continent <b></b>
DOI: 10.6084/m9.figshare.14905323
title: Table 15. Asian countries and number of articles published on each

description: Asian countries and number of articles published on each
DOI: 10.6084/m9.figshare.14905323.v1
title: Table 15. Asian countries and number of articles published on each

description: Asian countries and number of articles published on each
DOI: 10.6084/m9.figshare.14905341
title: Table 16. African countries and number of articles published on each

description: African countries and number of articles published on each
DOI: 10.6084/m9.figshare.14905341.v1
title: Table 16. African countries and number of articles published on each

description: African countries and number of articles published on each
DOI: 10.5281/zenodo.5068090
title: Harbert_Lab_SURE2021

description: Research data for Summer Undergraduate Research Experience program research 2021
DOI: 10.6084/m9.figshare.14905359
title: Table 17. European countries and number of DC articles published on each

description: European countries and number of DC articles published on each
DOI: 10.6084/m9.figshare.14905359.v1
title: Table 17. European countries and number of DC articles published on each

description: European countries and number of DC articles published on each
DOI: 10.6084/m9.figshare.14905371
title: Table 18. South American countries and number of articles published on each

description: South American countries and number of articles published on each
DOI: 10.6084/m9.figshare.14905371.v1
title: Table 18. South American countries and number of articles published on each

description: South American countries and number of articles published on each
DOI: 10.34740/kaggle/dsv/2392226
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.6084/m9.figshare.14905398
title: Table 20. North America countries and number of articles published on each

description: North America countries and number of articles published on each
DOI: 10.6084/m9.figshare.14905398.v1
title: Table 20. North America countries and number of articles published on each

description: North America countries and number of articles published on each
DOI: 10.5281/zenodo.5068129
title: Harbert_Lab_SURE2021

description: Research data for Summer Undergraduate Research Experience program research 2021
DOI: 10.6084/m9.figshare.14905425
title: Table 22. Methodology employed in Developing countries articles published

description: Methodology employed in Developing countries articles published
DOI: 10.6084/m9.figshare.14905425.v1
title: Table 22. Methodology employed in Developing countries articles published

description: Methodology employed in Developing countries articles published
DOI: 10.6084/m9.figshare.14885907
title: IDENT_MTL_2021

description: The data set is associated to the publication "No complementarity no gain - Net diversity effects on tree productivity occur once complementarity emerges during early stand development" by Urgoiti, Messier, Keeton, Reich, Gravel and Paquette. It provides data regarding the basal area at species level per plot and year from IDENT experiment of Montreal from 2009 to 2019. Information about block, plot and species richness is also given.
DOI: 10.34740/kaggle/dsv/2392308
title: Wikibooks Dataset
DOI: 10.6084/m9.figshare.14905443
title: Table 23. Trend percentage for methodologies employed in developing countries articles

description: Trend percentage for methodologies employed in developing countries articles
DOI: 10.6084/m9.figshare.14905443.v1
title: Table 23. Trend percentage for methodologies employed in developing countries articles

description: Trend percentage for methodologies employed in developing countries articles
DOI: 10.6084/m9.figshare.14905449
title: Table 24. Research methods used in DC (based on country)

description: Research methods used in DC (based on country)<b></b>
DOI: 10.6084/m9.figshare.14905449.v1
title: Table 24. Research methods used in DC (based on country)

description: Research methods used in DC (based on country)<b></b>
DOI: 10.6084/m9.figshare.14905464
title: Table 25. Research methods used in DC (% of Qualitative to Total articles per country)

description: Research methods used in DC (% of Qualitative to Total articles per country)
DOI: 10.6084/m9.figshare.21303460
title: Additional file 4 of Pseudomonas aeruginosa biofilm dispersion by the mouse antimicrobial peptide CRAMP

description: Additional file 4. Differentially metabolic substance data.
DOI: 10.6084/m9.figshare.21303460.v1
title: Additional file 4 of Pseudomonas aeruginosa biofilm dispersion by the mouse antimicrobial peptide CRAMP

description: Additional file 4. Differentially metabolic substance data.
DOI: 10.6084/m9.figshare.21303461
title: Additional file 14 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 14: Table S1. Cuproptosis-regulated genes list.
DOI: 10.6084/m9.figshare.21303461.v1
title: Additional file 14 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 14: Table S1. Cuproptosis-regulated genes list.
DOI: 10.6084/m9.figshare.21303464
title: Additional file 15 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 15: Table S2. The somatic mutation of CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.21303464.v1
title: Additional file 15 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 15: Table S2. The somatic mutation of CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.21303466
title: Additional file 1 of MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

description: Additional file 1: Includes 11 tables for training and testing data sets used in the study. The names of the tables are: Table S1. Training variants for MetaRNN; Table S2. Training variants for MetaRNN-indel; Table S3. Summary statistics for test datasets used to evaluate MetaRNN; Table S4. RNTS validation data set for MetaRNN model evaluation; Table S5. RCTS validation data set for MetaRNN model evaluation; Table S6. AF-RNTS validation data set for MetaRNN model evaluation; Table S7. AAFS validation data set for MetaRNN model evaluation; Table S8. TP53 validation data set for MetaRNN model evaluation; Table S9. Cancer somatic hotspot validation data set for MetaRNN model evaluation; Table S10. ClinVar validation data set for MetaRNN-indel model evaluation; Table S11. Search space of hyperparameters for MetaRNN and MetaRNN-indel.
DOI: 10.6084/m9.figshare.21303466.v1
title: Additional file 1 of MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

description: Additional file 1: Includes 11 tables for training and testing data sets used in the study. The names of the tables are: Table S1. Training variants for MetaRNN; Table S2. Training variants for MetaRNN-indel; Table S3. Summary statistics for test datasets used to evaluate MetaRNN; Table S4. RNTS validation data set for MetaRNN model evaluation; Table S5. RCTS validation data set for MetaRNN model evaluation; Table S6. AF-RNTS validation data set for MetaRNN model evaluation; Table S7. AAFS validation data set for MetaRNN model evaluation; Table S8. TP53 validation data set for MetaRNN model evaluation; Table S9. Cancer somatic hotspot validation data set for MetaRNN model evaluation; Table S10. ClinVar validation data set for MetaRNN-indel model evaluation; Table S11. Search space of hyperparameters for MetaRNN and MetaRNN-indel.
DOI: 10.6084/m9.figshare.21303467
title: Additional file 16 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 16: Table S3. The somatic copy number alteration of CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.21303467.v1
title: Additional file 16 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 16: Table S3. The somatic copy number alteration of CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.21303470
title: Additional file 17 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 17: Table S4. Differentially expressed CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.21303470.v1
title: Additional file 17 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 17: Table S4. Differentially expressed CRGs in pan-cancer.
DOI: 10.6084/m9.figshare.17109167
title: Mass-wasting-inferred Dramatic Variability of 130,000-year Indian Summer Monsoon Intensity from Deposits in the Southeast Tibetan Plateau

description: A large proportion (~80%)
of the Indian subcontinent’s precipitation comes from the Indian summer monsoon
(ISM), which influences one-fifth of the world’s population. A long-term
reliable proxy for ISM is fundamental to understanding previous global climate change.
We establish a mass-wasting-inferred proxy to examine the
paleohydrogeology (river undercutting history) of the Southeast Tibetan
Plateau and reconstruct the dramatic variability of ISM intensity (precipitation) in the
past 130,000 years. Our data suggest that mass-wasting events, which provides
us sufficient samples for paleoclimate research, are prone to dramatic climate
changes, especially extreme climate environments. The Southeast Tibetan Plateau
was subjected to at least four distinct ISM intensity phases in the past
130,000 years. We conclude that ISM intensity has a cyclicity featured by the
Earth’s orbit with obliquity, and that ISM intensity
transition lags the global ice volume (sea
level) change by 8–15 kyrs.
DOI: 10.6084/m9.figshare.21303473
title: Additional file 18 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 18: Table S5. Subtype distribution of CRGs in multiple cancers.
DOI: 10.6084/m9.figshare.21303473.v1
title: Additional file 18 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 18: Table S5. Subtype distribution of CRGs in multiple cancers.
DOI: 10.6084/m9.figshare.21303476
title: Additional file 19 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 19: Table S6. CEScore in pan-cancer.
DOI: 10.6084/m9.figshare.21303476.v1
title: Additional file 19 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 19: Table S6. CEScore in pan-cancer.
DOI: 10.6084/m9.figshare.17109176
title: A novel fungal strain, <i>Bjerkandera adusta</i> BK-1, as a promising broad-spectrum biological control agent against plant fungal diseases

description: In this study, we aimed to investigate the biopesticide activity of a novel fungal strain <i>Bjerkandera adusta</i> BK-1. The <i>in vitro</i> inhibitory potential of BK-1 against six species of identified phytopathogenic fungi was evaluated using a dual culture assay. In addition, <i>in-planta</i> treatment with BK-1 as a fungicide significantly reduced plant disease symptoms, such as grey mould, brown spot, and early blight disease. Furthermore, the colonisation ability of BK-1 in soil and leaves was also found to be stable. Overall, BK-1 has practical value as a novel, promising broad-spectrum biocontrol agent against multiple plant fungal diseases.
DOI: 10.6084/m9.figshare.17109176.v1
title: A novel fungal strain, <i>Bjerkandera adusta</i> BK-1, as a promising broad-spectrum biological control agent against plant fungal diseases

description: In this study, we aimed to investigate the biopesticide activity of a novel fungal strain <i>Bjerkandera adusta</i> BK-1. The <i>in vitro</i> inhibitory potential of BK-1 against six species of identified phytopathogenic fungi was evaluated using a dual culture assay. In addition, <i>in-planta</i> treatment with BK-1 as a fungicide significantly reduced plant disease symptoms, such as grey mould, brown spot, and early blight disease. Furthermore, the colonisation ability of BK-1 in soil and leaves was also found to be stable. Overall, BK-1 has practical value as a novel, promising broad-spectrum biocontrol agent against multiple plant fungal diseases.
DOI: 10.6084/m9.figshare.21303482
title: Additional file 20 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 20: Table S7. Cox analysis of CEScore in pan-cancer.
DOI: 10.6084/m9.figshare.21303482.v1
title: Additional file 20 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

description: Additional file 20: Table S7. Cox analysis of CEScore in pan-cancer.
DOI: 10.15468/dl.shreyv
title: Occurrence Download

description: A dataset containing 3186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scandix pecten-veneris",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3186 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014889-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/usxmeu/u2oben
title: C1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/2lwanp
title: F1_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/2drnk2
title: K1_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/yrmqti
title: J1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/5wya18
title: D1_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/fr6rlt
title: C2_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/qhbpio
title: B1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/rdglqi
title: B1_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/r0hrsr
title: G2_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/t3yegs
title: F2_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/abefhv
title: 12003_GEUS_Aeromag_2012_Final_Data.gdb

description: :unav
DOI: 10.22008/fk2/usxmeu/yhst71
title: A2_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/cu4fru
title: A2_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/xza6bb
title: H1_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/k296wp
title: D1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/flyprr
title: K2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/peusa4
title: B2_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/bg5cie
title: A1_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/jqnbgs
title: H2_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/b7dw1s
title: J2_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/nsgpno
title: D1_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/vcsayx
title: B2_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/7cz2h5
title: K1_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/0b6p60
title: K2_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/euq7oc
title: D1_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/c1tldc
title: K1_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/sisabj
title: 12003_GEUS_SVM.gxf

description: :unav
DOI: 10.22008/fk2/usxmeu/ijrnqy
title: G1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/e60hdd
title: 12003_GEUS_TMFI.gxf

description: :unav
DOI: 10.22008/fk2/usxmeu/kmtiyj
title: G2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/ud2tkb
title: D1_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/isf1vb
title: 12003_GEUS_Aeromag_2012_Final_Data.XYZ

description: :unav
DOI: 10.22008/fk2/usxmeu/rkxuqn
title: B2_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/tgyjv5
title: A2_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/bzhk25
title: VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/mon9qh
title: I1_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/uhxfgp
title: I2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/blnzed
title: K2_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/yyddng
title: F1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/hwcda2
title: A1_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/28cdpx
title: H1_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/gzsuyi
title: TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/qmjevc
title: H1_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/bldvf9
title: B1_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/yulyp4
title: E2_VG1.map

description: :unav
DOI: 10.22008/fk2/usxmeu/jr5rys
title: L1_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/ptte77
title: I2_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/kn9l5o
title: G1_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/gk0w91
title: D2_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/65wmb3
title: F1_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/d54num
title: H2_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/2z6cvt
title: F2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/pnitle
title: K1_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/k1cone
title: D1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/vj2w4n
title: J1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/1hpdit
title: L1_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/xgqb72
title: vg1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/aycutt
title: L1_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/psl0nm
title: E2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/y8kods
title: 12003_GEUS_TMFI.grd

description: :unav
DOI: 10.22008/fk2/usxmeu/o617vk
title: C1_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/dybpql
title: E2_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/dynpm5
title: I2_Flight_Path.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/bwrcji
title: G1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/8tcryx
title: B2_TMF.map

description: :unav
DOI: 10.22008/fk2/usxmeu/103rcz
title: E2_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/k77t0a
title: K2_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/phyysv
title: I1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/e5f7i9
title: F2_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/xtglbj
title: H2_VG2.map

description: :unav
DOI: 10.22008/fk2/usxmeu/wvg1or
title: F2_TMF.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/b4rl9l
title: K1_VG1.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/mzahmc
title: B1_Flight_Path.map

description: :unav
DOI: 10.22008/fk2/usxmeu/vlvxrb
title: A1_VG2.pdf

description: :unav
DOI: 10.22008/fk2/usxmeu/jte9wz
title: C1_Flight_Path.map

description: :unav