cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18710/usxcrs
title: Replication Data for: Secretomic analysis of extracellular vesicles originating from polyomavirus-negative and polyomavirus-positive Merkel cell carcinoma cell lines

description: This dataset is about analyses of two Merkel cell polyomavirus (MCPyV)-negative and two MCPyV–positive Merkel cell lines secretomic proteins.
description: &lt;/p&gt;Abstract: Extracellular vesicles or exosomes constitute an evolutionarily conserved mechanism of intercellular signaling. Exosomes are gaining an increasing amount of attention due to their role
in pathologies, including malignancy, their importance as prognostic and diagnostic markers,
and their potential as a therapeutic tool. Merkel cell carcinoma (MCC) is an aggressive form of
skin cancer with a poor prognosis. Because an effective systemic treatment for this cancer type
is currently not available, an exosome-based therapy was proposed. However, comprehensive
secretome profiling has not been performed for MCC. To help unveil the putative contribution
of exosomes in MCC, we studied the protein content of MCC-derived exosomes. Since approximately
80% of all MCC cases contain Merkel cell polyomavirus (MCPyV), the secretomes of
two MCPyV-negative and two MCPyV-positive MCC cell lines were compared. We identified
with high confidence 164 exosome-derived proteins common for all four cell lines that were annotated
in ExoCarta and Vesiclepedia databases. These include proteins implicated in motility,
metastasis and tumor progression, such as integrins and tetraspanins, intracellular signaling
molecules, chaperones, proteasomal proteins, and translation factors. Additional virus-negative
and virus-positiveMCC cell lines should be examined to identify highly representative exosomal
proteins that may provide reliable prognostic and diagnostic biomarkers, as well as targets for
treatment in the future. Data are available via ProteomeXchange with identifier PXD004198.
DOI: 10.18710/usxcrs/iknwib
title: Supporting_Information_Table 5.txt
DOI: 10.18710/vqhynu
title: Hydrographic data from Northern Norwegian fjords - 1959

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
description: Hydrographic data from Northern Norwegian fjords
DOI: 10.18710/xg6cyw/aeuduf
title: 01_ReadMe_Irish_CWC_survey.txt
DOI: 10.18710/4ldom0
title: Hydrographic data from Northern Norwegian fjords - 1984

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
DOI: 10.18710/925gny/xdk7zb
title: Scan2.txt

description: A second analysis of the same texts by the same three researchers, carried out after a comparison of responses in Scan1
DOI: 10.18710/av9r5x/sumsbx
title: Norwegian_coastal_radiocarbon_date.tab
DOI: 10.18710/bdmyik
title: Replication Data for: Comparative analysis of microRNA expression profiles of exosomes derived from polyomavirus-negative and –positive Merkel cell lines by next generation sequencing

description: This dataset is about the differentially expressed exosomal microRNA between MCPyV-negative and -positive MCC cell lines.
description: &lt;/p&gt;Abstract: MicroRNAs (miRNAs) are small non-coding RNAs responsible for post-transcriptional regulation of gene expression through interaction with messenger RNAs (mRNAs). In the past few years, evidence of the presence of cellular miRNAs in extracellular human body fluids such as serum, plasma, saliva, and urine has accumulated. Also, miRNAs have found in membrane-bound vesicles such as exosomes. They are involved in critical biological processes and dysregulated in a variety of diseases, including cancer and infections. Although little known about the role of exosomal miRNAs, it has demonstrated that miRNAs secreted by virus-infected cells transferred to and act in uninfected recipient cells, thereby contributing to spreading the pathogenic properties of the virus. Merkel cell carcinoma (MCC) is skin cancer of neuroendocrine origin. The significant risk factors are ultraviolet light exposure and the presence of integrated Merkel cell polyomavirus (MCPyV) genome. In this work, we sequenced exosomal miRNAs of MCPyV-negative (MCC13 and MCC26) and –positive MCC (MKL1 and MKL2) cell lines. In total, 519 exosomal miRNA were identified as a differentially expressed between the virus-negative and virus-positive cell lines and selected eight exosomal miRNA validated on exosomal serum/plasma healthy donors and MCC patients by qRT-PCR. miR-222-3p target genes predicted using the ExoCarta, TargetScan, and miRTarBase databases to understand the exosomal miR-222-3p influence in health and disease. The result showed the miR-222-3p is present in exosomes from Merkel cell carcinoma cell culture, serum of healthy donors and plasma of MCC patients. The sequencing result indicated that miR-222-3p is more abundant in exosomes generated by virus-negative MCC cells than in exosomes secreted by virus-positive MCC cell lines. qRT-PCR validation confirmed this finding — however, the miR-222-3p presence at a higher level in exosomes in healthy condition than in pathophysiological state. The enrichment analysis showed 11 of predicted 20 common miR-222-3p targets expressed in leukocytes and 6 of them identified as differentially expressed proteins in MCC cell lines. The role of miR-222-3p as the messenger in the cell-to-cell communication in the cancer environment and circulation discussed.
DOI: 10.18710/bdmyik/vdixal
title: Supplementary_Material_Table_S1.txt
DOI: 10.18710/c9ozwv/0x0ksj
title: CAMB3LYP_pcm_Rh6G_frame34_631pGs_frame34_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/4ao0eq
title: frame76_mmfq.pcm
DOI: 10.18710/c9ozwv/dcli3v
title: CAMB3LYP_pcm_Rh6G_frame66_631pGs_frame66_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/g331pa
title: frame21.xyz
DOI: 10.18710/c9ozwv/gindi9
title: frame82_mmfq.pcm
DOI: 10.18710/c9ozwv/jgikh6
title: frame43_mmfq.pcm
DOI: 10.18710/c9ozwv/kr74nr
title: frame79_mmfq.pcm
DOI: 10.18710/c9ozwv/oa3bqx
title: frame89_mmfq.pcm
DOI: 10.18710/c9ozwv/yll66c
title: frame92_mmfq.pcm
DOI: 10.18710/c9ozwv/yujk7b
title: frame99.mol
DOI: 10.18710/gxmk7k/tbsdkb
title: S3b_compensated_mag.tab

description: Near-seafloor magnetic data from the axial volcanic ridge (see location map)
DOI: 10.18710/kcw912/465kzy
title: Supporting_Information_Table_S14.tab
DOI: 10.18710/kcw912/6xgczo
title: Supporting_Information_Table_S4.txt
DOI: 10.18710/kcw912/mbgzqf
title: Supporting_Information_Table_S11.tab
DOI: 10.18710/kcw912/p0eent
title: Supporting_Information_Table_S20.tab
DOI: 10.18710/kcw912/p14lmd
title: Supporting_Information_Table_S9.txt
DOI: 10.18710/kcw912/rubjpf
title: Supporting_Information_Table_S10.txt
DOI: 10.18710/kcw912/zyosfd
title: Supporting_Information_Table_S7.txt
DOI: 10.18710/prjw7b/lml610
title: figure2_20160630_0940-1000_maarsy_az327ze12mat_snr.txt

description: Radar 12 degree tilt
DOI: 10.18710/roz2qy/hp8pau
title: 02_ReadMe_1.pdf

description: Description in .pdf format
DOI: 10.18710/saoag5/pkenvx
title: reindeer_density.txt

description: raw data called upon from R-script
DOI: 10.18710/saoag5/wok7oc
title: Metadata.txt

description: Description of all variables in all raw data files (txt format)
DOI: 10.18710/usxcrs/gyufpq
title: 01_ReadMe.txt

description: An overview and short description of the files contained in dataset, which icludes the contact information, what the dataset is about, column headings in tabular data, some abbreviations, and where to find which data = overview of files.
DOI: 10.18710/6juyey/tkiwoo
title: Subset5_Fixlmillner_682114.tab
DOI: 10.18710/bffmph/016rou
title: MGLU.txt

description: This is the original MGLU=MSLU text.
DOI: 10.18710/bffmph/dt00ky
title: README.pdf

description: This file describes the structure and purpose of all of the other files in this post.
DOI: 10.18710/c9ozwv/anrbsj
title: frame100_mmfq.pcm
DOI: 10.18710/c9ozwv/enrqsz
title: frame67.xyz
DOI: 10.18710/c9ozwv/kr1wcp
title: CAMB3LYP_pcm_Rh6G_frame100_631pGs_frame100_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/krx9ot
title: frame93_mmfq.pcm
DOI: 10.18710/c9ozwv/oauhxt
title: frame13.xyz
DOI: 10.18710/c9ozwv/ozllek
title: CAMB3LYP_pcm_Rh6G_frame33_631pGs_frame33_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/rijtkp
title: CAMB3LYP_pcm_Rh6G_frame16_631pGs_frame16_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/tvtpdw
title: frame18_mmfq.pcm
DOI: 10.18710/c9ozwv/wvzxoi
title: CAMB3LYP_pcm_Rh6G_frame67_631pGs_frame67_mmfq_4_2_10.out
DOI: 10.18710/dsapap/rt2ayk
title: Interbreeding_cleaner_fish_Female.txt
DOI: 10.18710/jtohcc
title: Hydrographic data from Northern Norwegian fjords - 1979

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
description: Hydrographic data from Northern Norwegian fjords
DOI: 10.18710/kcw912/anyg1l
title: 01_ReadMe.txt

description: An overview and short description of the files contained in dataset, which icludes the contact information, what the dataset is about, column headings in tabular data, some abbreviations, and where to find which data = overview of files.
DOI: 10.18710/kcw912/nncdnz
title: Supporting_Information_Table_S13.txt
DOI: 10.18710/lemxbu/6wqxuw
title: ne_faraday_MXD1.tab
DOI: 10.18710/pbywj0
title: Replication Data for: Observing mesospheric turbulence with specular meteor radars: A novel method for estimating second order statistics of wind velocity

description: There are few observational techniques for measuring the distribution of kinetic energy within the mesosphere with a wide range of spatial and temporal scales. This study describes a method for estimating the three-dimensional mesospheric wind field correlation function from specular meteor trail echoes. Each radar echo provides a measurement of a one-dimensional projection of the wind velocity vector at a randomly sampled point in space and time. The method relies on using pairs of such measurements to estimate the correlation function of the wind with different spatial and temporal lags. The method is demonstrated using a multistatic meteor radar data set that includes 100000 meteor echoes observed during a 24 hour time period. The new method is found to be in good agreement with the well established technique for estimating horizontal mean winds. High resolution correlation functions with temporal, horizontal, and vertical lags are also estimated from the data. The temporal correlation function is used to retreive the kinetic energy spectrum,
which includes the semidiurnal mode and a three-hour period wave. The horizontal and vertical correlation functions of the wind are then used to derive second order structure functions, which are found to be compatible with the Kolmogorov prediction for spectral distribution of kinetic energy in the turbulent inertial range. The presented method can be used to extend the capabilities of specular meteor radars. It is relatively flexible and has a multitude of applications beyond what has been shown in this study. 

The dataset contains radial Doppler shifts and positions of meteor trails measured using specular meteor radars. The specular meteor radar system was a new experimental system that was operated in Kühlungsborn, Germany. The data spans a 24 hour period, between 2018-11-05T00:00:00Z to 2018-11-06T00:00:00Z. 

The measurements were generated with custom created software, which is described in the following article:

Vierinen, J., Chau, J. L., Pfeffer, N., Clahsen, M., and Stober, G.: Coded continuous wave meteor radar, Atmos. Meas. Tech., 9, 829-839, https://doi.org/10.5194/amt-9-829-2016, 2016.
DOI: 10.18710/pbywj0/x8rlzi
title: 00_ReadMe.txt
DOI: 10.18710/c9ozwv/xrvtfh
title: frame24.xyz
DOI: 10.18710/kcw912/n3lwrn
title: Supporting_Information_Table_S18.tab
DOI: 10.18710/lemxbu/u5xzfz
title: G2.txt
DOI: 10.18710/ogqoqw/yrnsco
title: Supplementary_Table_4.txt
DOI: 10.18710/roz2qy/tw7ujz
title: 01_ReadMe_1.txt

description: Description
DOI: 10.18710/usxcrs/mcwnrv
title: Supporting_Information_Table 6.txt
DOI: 10.18710/6juyey
title: Replication Data for: Did Astronomy Constitute a Denominationally Neutral Space within the Republic of Letters? An Outline for the Use of Visualization Tools in the Study of Astronomical Correspondence

description: This dataset consists of metadata used for creating the visualizations accompanying our article “Did Astronomy Constitute a Denominationally Neutral Space within the Republic of Letters? An Outline for the Use of Visualization Tools in the Study of Astronomical Correspondence”, to be published in July 2019.
DOI: 10.18710/6juyey/5fj99p
title: readme-file_replication-data-for_DidAstronomyConstituteAConfessionallyNeutralSpace.pdf
DOI: 10.18710/6juyey/jvodas
title: Subset4_Hell_671845-1.tab
DOI: 10.18710/6juyey/re4i7o
title: Subset3_Fixlmillner-correspondence_before-and-after_1773.tab
DOI: 10.18710/bdmyik/e9ectq
title: Supplementary_Material_Table_S3A.txt
DOI: 10.18710/bdmyik/eoa2ih
title: Supplementary_Material_Table_S3B.txt
DOI: 10.18710/bdmyik/lyviql
title: Supplementary_Material_Table_S5.txt
DOI: 10.18710/bdmyik/oivlsi
title: Supplementary_Material_Table_S5.tab
DOI: 10.18710/bdmyik/umwwwv
title: Supplementary_Material_Table_S4.txt
DOI: 10.18710/bdmyik/ysugku
title: Supplementary_Material_Table_S6.txt
DOI: 10.18710/bobhpl
title: Hydrographic data from Northern Norwegian fjords - 1986

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
description: Hydrographic data from Northern Norwegian fjords
DOI: 10.18710/c9ozwv/2shosp
title: frame23_mmfq.pcm
DOI: 10.18710/c9ozwv/2taf06
title: frame100_mmfq.pcm
DOI: 10.18710/c9ozwv/6mk0mw
title: frame84_mmfq.pcm
DOI: 10.18710/c9ozwv/b3t0ml
title: frame91.xyz
DOI: 10.18710/c9ozwv/edweth
title: frame60_mmfq.pcm
DOI: 10.18710/c9ozwv/l8vlhj
title: CAMB3LYP_pcm_Rh6G_frame78_631pGs_frame78_mmfq_4_2_10.out
DOI: 10.18710/c9ozwv/lgisxa
title: frame3_mmfq.pcm
DOI: 10.18710/c9ozwv/sv7xxs
title: frame26_mmfq.pcm
DOI: 10.18710/c9ozwv/top9il
title: frame30.xyz
DOI: 10.18710/cxrcuw
title: Replication Data for: Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

description: Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.&lt;br&gt;&lt;br&gt;
description: Abstract: Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R2validation=0.94 and RMSEP=0.20 % for N, R2validation=0.76 and RMSEP=0.05 % for P and R2validation=0.82 and RMSEP=1.16 % for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.
DOI: 10.18710/e0ibyf
title: Multicomponent ocean bottom seismic (OBS) data from 10 stations along the Vestnesa Ridge, W-Svalbard margin

description: The data include multicomponent ocean bottom seismic (OBS) records from 10 stations along the Vestnesa Ridge. This data is used in the article "Constraints on Gas Hydrate Distribution and Morphology in Vestnesa Ridge, Western Svalbard Margin, Using Multicomponent Ocean‐Bottom Seismic Data". Further details about the processing and seismic velocity analysis from the data is included in this article.
DOI: 10.18710/kcw912/fcoron
title: Supporting_Information_Table_S6.txt
DOI: 10.18710/kcw912/qz3uxb
title: Supporting_Information_Table_S12.txt
DOI: 10.18710/kcw912/ynd8rq
title: Supporting_Information_Table_S18.txt
DOI: 10.18710/lemxbu/rtrldq
title: Height_RMR_along.txt
DOI: 10.18710/n8gf1u/oqmqul
title: DUSTY1B.txt

description: DUSTY 1 on MXD 1B
DOI: 10.18710/ogqoqw/zkj006
title: Supplementary_Table_4.xlsx
DOI: 10.18710/q3gfgg/iput0v
title: School dropout and mental health.dat

description: Data set
DOI: 10.18710/saoag5/umvzti
title: Metadata.docx

description: Description of all variables in all raw data files (docx format)
DOI: 10.18710/usxcrs/jrvcqy
title: Supporting_Information_Table 4.xlsx
DOI: 10.18710/usxcrs/mzchpq
title: Supporting_Information_Table 6.xlsx
DOI: 10.18710/xg6cyw/sx3q61
title: Irish_Pooled_Data.txt
DOI: 10.18710/y7qf58
title: Hydrographic data from Northern Norwegian fjords - 1988

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
description: Hydrographic data from Northern Norwegian fjords
DOI: 10.18710/18awwa
title: Hydrographic data from Northern Norwegian fjords - 1981

description: The periodic collection of hydrophysical data along the northern Norwegian coast (from Malangen to Porsangerfjord) started in the beginning of the 20th century and some of the oldest stations are still in operation by the “Havmiljødata” sampling programme organized by UIT-The Arctic university of Norway. Systematical retrieval of the physical oceanographical data during shorter periods (1–2 years) in Tromsø and north Norway goes back to ca. 1872 when Tromsø Museum was founded. The first decades sampling was performed with hired vessels, and retrieval of hydrophysical data was connected to sampling of marine organisms. From ca. 1975 it was taken over by the University of Tromsø and from 1979 it was up running again with a new revised network. Up through the years much effort has also been put into data quality control and instrumentation (CTD) mantenance and calibration. From the early 1990s, the BFE faculty (Fakultet for biovitenskap, fiskeri og økonomi) that has had the responsibility for the data sampling organized as Havmiljødata (HMD).
description: Hydrographic data from Northern Norwegian fjords
DOI: 10.18710/18awwa/3bi0y3
title: 01_ReadMe.txt

description: Readme file
DOI: 10.18710/18awwa/5xh60k
title: hmd_data_1981.tab

description: Data file