cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3902314
title: Open-Ended Coaxial Cable Measurements in Diesel-Contaminated soil during Bioremediation

description: Complex dielectric permittivity was measured over a broad frequency range (200 MHz to 20 GHz) with Keysight 85070D dielectric probe kit connected to a network analyzer (Keysight N5227A), on a series of 6 soil microcosms contaminated with diesel oil, before and after a process of bioremediation. This technology measures separately the real and the imaginary component of complex dielectric permittivity, which are meaningful properties of soil.
description: {"references": ["Vergnano, A.; Godio, A.; Raffa, C.M.; Chiampo, F.; Tobon Vasquez, J.A.; Vipiana, F. Open-Ended Coaxial Probe Measurements of Complex Dielectric Permittivity in Diesel-Contaminated Soil during Bioremediation. Sensors 2020, 20, 6677."]}
DOI: 10.25646/7669
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.4284431
title: Entity Matching on Dataset Smartphone

description: Entity Matching on Dataset Smartphone
DOI: 10.6084/m9.figshare.13271072
title: Supplementary material.docx

description: Supplementary materials for study "Characterization of the relationship between olfactory perception and the release of aroma compounds before and after simulated oral processing"
DOI: 10.5281/zenodo.4284349
title: Techmaster event log

description: Techmaster log for the replication of experiments described in Context-Aware Process Performance Indicator Prediction in IEEE Access 2020.
DOI: 10.5281/zenodo.4284348
title: Techmaster event log

description: Techmaster log for the replication of experiments described in Context-Aware Process Performance Indicator Prediction in IEEE Access 2020.
DOI: 10.6084/m9.figshare.13271066
title: The determination of UGPG and GMPPB reaction systems.Putative genes related to phosphotransferase system (PTS) in L. paracasei TD 062.

description: The determination of UGPG and GMPPB reaction systems. Putative genes related to phosphotransferase system (PTS) in <i>L. paracasei </i>TD 062.
DOI: 10.6084/m9.figshare.13271066.v2
title: The determination of UGPG and GMPPB reaction systems.Putative genes related to phosphotransferase system (PTS) in L. paracasei TD 062.

description: The determination of UGPG and GMPPB reaction systems. Putative genes related to phosphotransferase system (PTS) in <i>L. paracasei </i>TD 062.
DOI: 10.6084/m9.figshare.13271066.v1
title: The determination of UGPG and GMPPB reaction systems.Putative genes related to phosphotransferase system (PTS) in L. paracasei TD 062.

description: The determination of UGPG and GMPPB reaction systems. Putative genes related to phosphotransferase system (PTS) in <i>L. paracasei </i>TD 062.
DOI: 10.5281/zenodo.4284483
title: Reference data for MITOS2

description: Reference data for MITOS2 derived from RefSeq63 and 81 separately for Metazoa (m), Fungi (f), and Opisthokonta (o). Contains - fasta databases for the BLAST searches - automatically derived covariance models for RNA searches - for refseq63m hidden markov models for protein prediction (http://dx.doi.org/10.1016/j.ympev.2016.09.024)
description: Updates refseq39 reference data: adds missing auxinfo.json file
DOI: 10.5281/zenodo.4284439
title: Datasets for ``The effect of a dynamo-generated field on the Parker wind''

description: <pre>This directory contains an index.html file with links to the run directories for Runs A-C and idl plotting routines with secondary data for the other figures for the paper "The effect of a dynamo-generated field on the Parker wind" by Jakab & Brandenburg.</pre>
DOI: 10.5281/zenodo.4284438
title: Datasets for ``The effect of a dynamo-generated field on the Parker wind''

description: <pre>This directory contains an index.html file with links to the run directories for Runs A-C and idl plotting routines with secondary data for the other figures for the paper "The effect of a dynamo-generated field on the Parker wind" by Jakab & Brandenburg.</pre>
DOI: 10.6084/m9.figshare.12987266
title: Tables

description: <b>PONE-D-20-16521</b><b></b>
DOI: 10.6084/m9.figshare.12987266.v1
title: Tables

description: <b>PONE-D-20-16521</b><b></b>
DOI: 10.6084/m9.figshare.12987533
title: Data

description: <b>PONE-D-20-16521</b>
DOI: 10.6084/m9.figshare.12987533.v1
title: Data

description: <b>PONE-D-20-16521</b>
DOI: 10.21227/aajs-me79
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 266,670 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/y26b-1143
title: Subjective and Objective Quality Assessment of the SoftCast Video Transmission Scheme

description: SoftCast-based linear video coding and transmission (LVCT) schemes have been proposed as a promising alternative to traditional video coding and transmission schemes in wireless environments. Currently, the performance of LVCT schemes is evaluated by means of traditional objective scores such as PSNR or SSIM. Nevertheless, since the compression is performed in a very different way from traditional coding schemes such as HEVC, visual artifacts are also quite different and deserve to be subjectively assessed.In this paper, we propose a subjective quality assessment of SoftCast, pioneer and standard of the LVCT schemes. This study aims to better understand the tradeoffs between the LVCT parameters that can be tuned to improve the quality. These parameters, including different GoP-sizes, Compression Ratios (CR) and Channel Signal-to-Noise Ratio (CSNR), are used to generate a dataset of 85 videos. A Double Stimulus Impairment Scale (DSIS) test is performed on the received videos to assess the perceived quality. Results show that the key characteristic of SoftCast, the linear relation between CSNR and PSNR, is also observed with the Mean-Opinion Scores (MOS), except at high CSNR where the quality saturates. In addition, Bjøntegaard model is used to quantify the trade-offs between CR, GoP-size and CSNR, depending on the intended application. Finally, the performance of objective metrics compared to the obtained MOS is evaluated. Results show that Multi-Scale SSIM (MS-SSIM), SSIM and Video Multimethod Assessment Fusion (VMAF) metrics offer the best correlation with the MOS values.
DOI: 10.6084/m9.figshare.13271009
title: q.docx

description: Sup 1
DOI: 10.6084/m9.figshare.13271009.v1
title: q.docx

description: Sup 1
DOI: 10.5519/qd.cejx0d2t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1417 records)
DOI: 10.34725/dvn/qaj8w8/ysbqvn
title: 2.Uganda_plot_form_subplots.xlsx

description: Sub plots data
DOI: 10.34725/dvn/qaj8w8/8eh0if
title: 1.Uganda_plot_form_main.xlsx

description: Main variables data
DOI: 10.34725/dvn/qaj8w8/26yy6z
title: 3.Uganda_plot_form_treeheight.xlsx

description: Tree height data
DOI: 10.34725/dvn/qaj8w8/zlw9qn
title: 0.Plot_data_variable_information.tab

description: Description of variables within the data files
DOI: 10.34725/dvn/qaj8w8/w2lij0
title: 0.IKI_plot_form_v3.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qaj8w8/xcnqzu
title: 0.IKI_plot_form_v1.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qaj8w8/c4gnsz
title: 0.IKI_plot_form_v2.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qaj8w8/xvuvrd
title: 1.Uganda_plot_form_main.rds
DOI: 10.34725/dvn/qaj8w8/1piki1
title: 2.Uganda_plot_form_subplots.rds
DOI: 10.34725/dvn/qaj8w8/lau5wb
title: 1.Uganda_plot_form_main.RData
DOI: 10.34725/dvn/qaj8w8/gscaah
title: 2.Uganda_plot_form_subplots.RData
DOI: 10.34725/dvn/qaj8w8/l5aey3
title: 2.Uganda_plot_form_subplots.xlsx
DOI: 10.34725/dvn/qaj8w8/eqllhw
title: 1.Uganda_plot_form_main.xlsx
DOI: 10.34725/dvn/qaj8w8/s2a3zk
title: 3.Uganda_plot_form_treeheight.RData
DOI: 10.34725/dvn/qaj8w8/b57hsd
title: 3.Uganda_plot_form_treeheight.rds
DOI: 10.34725/dvn/qaj8w8/gwq7ua
title: 3.Uganda_plot_form_treeheight.xlsx
DOI: 10.34725/dvn/qaj8w8
title: Farm inventory surveys in Uganda: Plots data

description: Plots data from Uganda
DOI: 10.34725/dvn/qbmyni/lryjtq
title: IKI_tree_form_v4.xlsx
DOI: 10.34725/dvn/qbmyni/hq0n5q
title: IKI_tree_form_v2.xlsx
DOI: 10.34725/dvn/qbmyni/c3kfq8
title: IKI_tree_form_v3.xlsx
DOI: 10.34725/dvn/qbmyni/pcyscg
title: 3.Uganda_tree_form_stemsampling.xlsx

description: Stem sampling table
DOI: 10.34725/dvn/qbmyni/qcycip
title: 1.Uganda_tree_form_idvariables.xlsx

description: Main variables
DOI: 10.34725/dvn/qbmyni/gdd2cw
title: 2.Uganda_tree_form_treeinfo.xlsx

description: Tree information table
DOI: 10.34725/dvn/qbmyni/p17lmy
title: 4.Uganda_tree_form_climberinfo.xlsx

description: Climber information table
DOI: 10.34725/dvn/qbmyni/od0ipj
title: 5.Uganda_tree_form_figinfo.xlsx

description: Fig information table
DOI: 10.34725/dvn/qbmyni/cdgdqd
title: 0.IKI_tree_form_v3.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qbmyni/0qw4zz
title: 0.IKI_tree_form_v2.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qbmyni/eprnur
title: 0.IKI_tree_form_v4.xls

description: Questionnaire: ODK form
DOI: 10.34725/dvn/qbmyni/gah6dj
title: 0.Tree_data_variable_information.tab

description: Description of variables within the data files
DOI: 10.34725/dvn/qbmyni/6ntoxq
title: 3.Uganda_tree_form_stemsampling.xlsx
DOI: 10.34725/dvn/qbmyni/uwpln2
title: 2.Uganda_tree_form_treeinfo.rds
DOI: 10.34725/dvn/qbmyni/fa0gvy
title: 4.Uganda_tree_form_climberinfo.xlsx
DOI: 10.34725/dvn/qbmyni/lafyl5
title: 4.Uganda_tree_form_climberinfo.RData
DOI: 10.34725/dvn/qbmyni/6xzdcl
title: 5.Uganda_tree_form_figinfo.xlsx
DOI: 10.34725/dvn/qbmyni/4kaaxz
title: 5.Uganda_tree_form_figinfo.rds
DOI: 10.34725/dvn/qbmyni/jmwah1
title: 1.Uganda_tree_form_idvariables.xlsx
DOI: 10.34725/dvn/qbmyni/inf2fo
title: 3.Uganda_tree_form_stemsampling.rds
DOI: 10.34725/dvn/qbmyni/qv8lso
title: 4.Uganda_tree_form_climberinfo.rds
DOI: 10.34725/dvn/qbmyni/2sbues
title: 2.Uganda_tree_form_treeinfo.RData
DOI: 10.34725/dvn/qbmyni/zivtgd
title: 3.Uganda_tree_form_stemsampling.RData
DOI: 10.34725/dvn/qbmyni/gsv1cm
title: 5.Uganda_tree_form_figinfo.RData
DOI: 10.34725/dvn/qbmyni/1qniv6
title: 2.Uganda_tree_form_treeinfo.xlsx
DOI: 10.34725/dvn/qbmyni/jcypdz
title: 1.Uganda_tree_form_idvariables.rds
DOI: 10.34725/dvn/qbmyni/h8xlgi
title: 1.Uganda_tree_form_idvariables.RData
DOI: 10.34725/dvn/qbmyni
title: Farm inventory surveys in Uganda: Tree data

description: Tree surveys done in Uganda
DOI: 10.5519/qd.kjayfqd0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (106 records)
DOI: 10.34725/dvn/pjzrlz/b7eabi
title: Uganda_community_matrix_trees.xlsx

description: Community matrix for all tree species
DOI: 10.34725/dvn/pjzrlz/tvqzxn
title: Uganda_community_matrix_birdspecies.xlsx

description: Community matrix for bird species
DOI: 10.34725/dvn/pjzrlz/hpul28
title: Uganda_community_matrix_trees_exotic.xlsx

description: Community matrix for exotic tree species only
DOI: 10.34725/dvn/pjzrlz/4msrff
title: Uganda_environment_matrix.xlsx

description: Environment matrix
DOI: 10.34725/dvn/pjzrlz/cga0uz
title: Uganda_community_matrix_trees_indigenous.xlsx

description: Community matrix for indigenous tree species only
DOI: 10.34725/dvn/pjzrlz/ket0vs
title: Uganda_diversity_indices_birdspecies.xlsx

description: Diversity indices from bird survey
DOI: 10.34725/dvn/pjzrlz/ni59pm
title: Uganda_community_matrix_indigeneous_stemtotal.xlsx
DOI: 10.34725/dvn/pjzrlz/ch2yfa
title: Uganda_community_matrix_alltrees_binary.xlsx
DOI: 10.34725/dvn/pjzrlz/z6fdul
title: Uganda_community_matrix_exotic_binary.rds
DOI: 10.34725/dvn/pjzrlz/g62npx
title: Uganda_environment_matrix.rds
DOI: 10.34725/dvn/pjzrlz/i1z7jk
title: Uganda_community_matrix_exotic_stemtotal.xlsx
DOI: 10.34725/dvn/pjzrlz/p19f1y
title: Uganda_community_matrix_alltrees_binary.rds
DOI: 10.34725/dvn/pjzrlz/jqitgk
title: Uganda_community_matrix_exotic_count.RData
DOI: 10.34725/dvn/pjzrlz/q6cejz
title: Uganda_community_matrix_exotic_stemtotal.RData
DOI: 10.34725/dvn/pjzrlz/3gzmgz
title: Uganda_community_matrix_alltrees_count.xlsx
DOI: 10.34725/dvn/pjzrlz/u1jujx
title: Uganda_community_matrix_indigeneous_stemtotal.rds
DOI: 10.34725/dvn/pjzrlz/hh6spj
title: Uganda_community_matrix_alltrees_count.RData
DOI: 10.34725/dvn/pjzrlz/wof8vg
title: Uganda_community_matrix_exotic_count.xlsx
DOI: 10.34725/dvn/pjzrlz/ajnl0d
title: Uganda_community_matrix_exotic_stemtotal.rds
DOI: 10.34725/dvn/pjzrlz/yymadq
title: Uganda_community_matrix_indigeneous_stemtotal.RData
DOI: 10.34725/dvn/pjzrlz/f9kstd
title: Uganda_community_matrix_indigeneous_count.rds
DOI: 10.34725/dvn/pjzrlz/erwhah
title: Uganda_community_matrix_exotic_count.rds
DOI: 10.34725/dvn/pjzrlz/6auolj
title: Uganda_community_matrix_exotic_binary.RData
DOI: 10.34725/dvn/pjzrlz/hgbfxq
title: Uganda_community_matrix_alltrees_stemtotal.rds
DOI: 10.34725/dvn/pjzrlz/d0sgvg
title: Uganda_community_matrix_alltrees_count.rds
DOI: 10.34725/dvn/pjzrlz/p7r3c4
title: Uganda_community_matrix_alltrees_stemtotal.xlsx
DOI: 10.34725/dvn/pjzrlz/fcg7h7
title: Uganda_community_matrix_indigeneous_binary.RData
DOI: 10.34725/dvn/pjzrlz/q7dpr9
title: Uganda_community_matrix_indigeneous_binary.rds
DOI: 10.34725/dvn/pjzrlz/pfefbw
title: Uganda_community_matrix_indigeneous_count.RData
DOI: 10.34725/dvn/pjzrlz/wqfrzh
title: Uganda_community_matrix_indigeneous_binary.xlsx
DOI: 10.34725/dvn/pjzrlz/zgkxm4
title: Uganda_community_matrix_alltrees_binary.RData
DOI: 10.34725/dvn/pjzrlz/qm1gpn
title: Uganda_community_matrix_exotic_binary.xlsx
DOI: 10.34725/dvn/pjzrlz/lo4lb0
title: Uganda_community_matrix_alltrees_stemtotal.RData
DOI: 10.34725/dvn/pjzrlz/amnyg0
title: Uganda_environment_matrix.RData
DOI: 10.34725/dvn/pjzrlz/qners8
title: Uganda_environment_matrix.xlsx
DOI: 10.34725/dvn/pjzrlz/u0ptwv
title: Uganda_community_matrix_indigeneous_count.xlsx
DOI: 10.34725/dvn/pjzrlz
title: Farm inventory surveys in Uganda: Computed data files

description: Computed data files from data collected during farm inventory surveys in Uganda
DOI: 10.34740/kaggle/dsv/1671167
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.13271459.v1
title: Data Entry.xlsx

description: Data is collected on intelligence and creativity from individuals with intellectual disability
DOI: 10.6084/m9.figshare.13271459
title: Data Entry.xlsx

description: Data is collected on intelligence and creativity from individuals with intellectual disability
DOI: 10.5281/zenodo.4283946
title: Relations between access to sport and green spaces and Body Mass Index in the canton of Geneva

description: README.txt -------------------------------------------------<br> Science et Ingénierie de l'Environnement (SIE)<br> Bâtiment GR<br> EPFL ENAC SSIE-GE<br> CH-1015 Lausanne<br> *<br> *<br> ------------------------------------------------.<br> *<br> *<br> "Envdata.csv" contains the data used in the study entitled:<br> "Relations between access to sport and green spaces and Body Mass Index in the canton of Geneva", <br> (C. El Khoury,Y. Frischholz, B. Heutte, V. Remy, M. Schornoz, 2020)<br> *<br> *<br> -------------------------------------------------<br> *<br> *<br> Variables : Description [unit]<br> *<br> ID : Bus Santé participant's ID [-]<br> x_lv03, y_lv03 : Coordinates of participants residence (LV03) [m]<br> year_survey : Bus Santé participation year [-]<br> #SPORT10min : Density of sport infrastructures within a buffer of 10 minutes walk around residences [-]<br> dist_1SPORT : Direct distance to first sport infrastructure from residences [m]<br> meanNDVI10min : Mean NDVI within a buffer of 10 minutes walk (to remove biases due to large water bodies, the lake area was removed from the 10 minutes buffer) [m]<br> BMI_randorder : Values of BMI used in a randomized order for confidentiality reasons [kg/m2]<br> *<br> *<br> -------------------------------------------------
DOI: 10.5281/zenodo.4283945
title: Relations between access to sport and green spaces and Body Mass Index in the canton of Geneva

description: README.txt -------------------------------------------------<br> Science et Ingénierie de l'Environnement (SIE)<br> Bâtiment GR<br> EPFL ENAC SSIE-GE<br> CH-1015 Lausanne<br> *<br> *<br> ------------------------------------------------.<br> *<br> *<br> "Envdata.csv" contains the data used in the study entitled:<br> "Relations between access to sport and green spaces and Body Mass Index in the canton of Geneva", <br> (C. El Khoury,Y. Frischholz, B. Heutte, V. Remy, M. Schornoz, 2020)<br> *<br> *<br> -------------------------------------------------<br> *<br> *<br> Variables : Description [unit]<br> *<br> ID : Bus Santé participant's ID [-]<br> x_lv03, y_lv03 : Coordinates of participants residence (LV03) [m]<br> year_survey : Bus Santé participation year [-]<br> #SPORT10min : Density of sport infrastructures within a buffer of 10 minutes walk around residences [-]<br> dist_1SPORT : Direct distance to first sport infrastructure from residences [m]<br> meanNDVI10min : Mean NDVI within a buffer of 10 minutes walk (to remove biases due to large water bodies, the lake area was removed from the 10 minutes buffer) [m]<br> BMI_randorder : Values of BMI used in a randomized order for confidentiality reasons [kg/m2]<br> *<br> *<br> -------------------------------------------------
DOI: 10.25345/c5dj3w
title: MassIVE Reanalysis RMSV000000335.1 - Proteomic and Genomic Methylation Signatures of Idiopathic Subglottic Stenosis
title: LC-MS/MS data obtained from subglottic biopsies collected from 12 individuals with idiopathic subglottic stenosis, as well as 3 age-, sex-, and race/ethnicity-matched controls. All tissue donors were women of White race and non-Latino or Hispanic ethnicity. Sample preparation, mass spectrometry, and data analysis details are available in the published article (PMID: 32619300; DOI: 10.1002/lary.28851).
DOI: 10.5281/zenodo.4282423
title: Data for GRL "Tropical Stratospheric Circulation and Ozone Coupled to Pacific Multi-Decadal Variability" paper

description: Data of the CESM-WACCM sensitivity simulations. These simulations follow the REFC1 configuration from the Chemistry-Climate Model Initiative (CCMI), but for: fixed long-lived halogenated substances at year 1955 (REFC1-fODS); fixed long-lived halogens and nitrogen oxide emissions at year 1955 (REFC1-fODS-N2O); fixed stratospheric aerosol averaged over 1998–1999 (REFC1-fSAD); climatological sea surface temperatures (SSTs) and sea-ice concentrations (SICs) for the 1960–2010 period (REFC1-fSST); climatological SSTs and SICs for the 1960–2010 period, including a 28-months cyclical QBO (REFC1-fSST-QBO).
DOI: 10.5281/zenodo.4282422
title: Data for GRL "Tropical Stratospheric Circulation and Ozone Coupled to Pacific Multi-Decadal Variability" paper

description: Data of the CESM-WACCM sensitivity simulations. These simulations follow the REFC1 configuration from the Chemistry-Climate Model Initiative (CCMI), but for: fixed long-lived halogenated substances at year 1955 (REFC1-fODS); fixed long-lived halogens and nitrogen oxide emissions at year 1955 (REFC1-fODS-N2O); fixed stratospheric aerosol averaged over 1998–1999 (REFC1-fSAD); climatological sea surface temperatures (SSTs) and sea-ice concentrations (SICs) for the 1960–2010 period (REFC1-fSST); climatological SSTs and SICs for the 1960–2010 period, including a 28-months cyclical QBO (REFC1-fSST-QBO).
DOI: 10.6084/m9.figshare.12286715
title: Preanalytical variables that affect the outcome of cell-free DNA measurements

description: Fragments of cell-free DNA (cfDNA) in human body fluids often carry disease-specific alterations and are now widely recognized as ideal biomarkers for the detection and monitoring of genomic disorders, especially cancer, that are normally difficult to examine noninvasively. However, the conversion of promising research findings into tools useful in routine clinical testing of cancer has been a slow-moving process. A major reason is that the diagnostic sensitivity and specificity of cfDNA-based clinical assays are negatively impacted by a combination of suboptimal and inter-institutional differences in preanalytical procedures. The most prominent factors include: (i) a poor understanding of the biological factors that determine the characteristics of the cfDNA population in a biospecimen prior to collection, (ii) inattention to how cfDNA with different structures and physical properties are affected differently by a given preanalytical step, and (iii) the sheer number of possible conditions that can be selected from for each preanalytical step along with a continually expanding menu of commercial products that often show varying degrees of bias and efficiency. The convergence of these variables makes it difficult for research groups and institutions to reach a consensus on optimal preanalytical procedures and a challenging task to establish widely applied standards, which ultimately hamper the development of cfDNA assays that are fit for broad clinical implementation. In this review, we follow a systematic approach to explore the most confounding preanalytical factors that affect the outcome of cfDNA measurements.