cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14390768.v6
title: sleep-diary-sleepmeter

description: Annotated daily sleep diary over years of a subject (author and scientific researcher) with the non-24 circadian rhythm disorder.<br>The sleep diary was manually curated using the Sleepmeter app on Android (pro version). It is duly annotated with all the controlled factors such as use of light therapy, light exposure (morning? evening?), disturbances such as noise or appointments, diet composition and timing, melatonin use, etc.<br>This should be considered the baseline biomarker of the subject's sleep-wake patterns, it is the ground truth for actigraphy and other biomarkers. The sleep diary was duly curated to minimize errors and was updated continuously everyday, once at bedtime by tapping a widget to mark the fall asleep time (with a delay from bedtime, the exact fall asleep time may not be 100% accurate but it was corrected the morning after to try to match as much as possible the subjectively estimated sleep onset delay), and on wake up by tapping again the widget as soon as the subject woke up, and then noting all the relevant comments and tags.<br>Most tags and comments pertain to the day before, although some tags and comments pertain to what happens in the day after (in the latter case, this is usually denoted in the tag name).<br>Use the latest sleep diary, it contains the whole history. Other sleep diaries are past versions, which may have some differences such as renamed tags or missing comments that were added aposteriori (sometimes months later when the culprit of a major sleep disturbance was discovered, such as secondary pernio due to long covid).<br>The sleep diary currently covers almost 2 years. Keep in mind that during the period of 2020-2022, the data was acquired while in lockdown or pseudo-lockdown most of the time due to the SaRS-nCOV-2 (COVID-19) pandemic. More data will be added in the future.<br>The files contain the whole history up to the timestamp in the filename. Hence, to study the longest period, use only the latest file, do not concatenate with previous ones.
DOI: 10.6084/m9.figshare.14384832.v12
title: axivity-ax6-left-forearm-nondominant

description: 6-axis actigraphy (translation and rotation) using Axivity AX6 (similar to AX3 used in UK BioBank), configured with 100Hz sampling frequency, 16bit resolution and 2000dps gyro/rotation.<br>Attached with a velcro to the exterior of a cotton wrist band with arrow facing down towards the ground/back when arms are in a natural position along the body, on the left forearm (non-dominant arm).<br>Started acquisition in March 2021.<br>Each file represents one acquisition session of about 1 week. Multiple files can be concatenated to study a longer time period.
DOI: 10.6084/m9.figshare.14384832.v11
title: axivity-ax6-left-forearm-nondominant

description: 6-axis actigraphy (translation and rotation) using Axivity AX6 (similar to AX3 used in UK BioBank), configured with 100Hz sampling frequency, 16bit resolution and 2000dps gyro/rotation.<br>Attached with a velcro to the exterior of a cotton wrist band with arrow facing down towards the ground/back when arms are in a natural position along the body, on the left forearm (non-dominant arm).<br>Started acquisition in March 2021.<br>Each file represents one acquisition session of about 1 week. Multiple files can be concatenated to study a longer time period.
DOI: 10.6084/m9.figshare.16780309.v2
title: Remote Sensing-based Field-Level Open Biomass Burning NOx Emission Inventory of China

description: Accurate estimation of nitrogen oxide (NOx) emissions from biomass combustion is of great significance for ecological protection and climate change prediction in China. In this study, an emission factor (EF) weighting strategy considering the prior probability of the elaborate crop distribution within the fire pixels was proposed. under the assumption of Gaussian distribution of diurnal cycle of FRP (fire radiative power), we estimate the emission inventory of NOx from biomass combustion on field-level of China. To validate the assumptions of Gaussian distribution of diurnal cycle of FRP, we compared our results with the Himawari-8 satellite observations, the Pearson correlation coefficient reached 0.65. Meanwhile, in order to evaluate the emission inventory, we compared our estimation with global fire emission database (GFED), in-situ observations and ozone monitoring instrument (OMI) product, the result shows that our emission inventory with high spatiotemporal resolution agreed well with GFED database and in-situ observations in general. In conclusion, the method proposed in this study can compensate the shortage of previous studies on lacking elaborate landcovers, especially crop types while estimating the emissions, which is of great significance for countermeasure of controlling agricultural-source air pollution
DOI: 10.6084/m9.figshare.16780309.v1
title: Remote Sensing-based Field-Level Open Biomass Burning NOx Emission Inventory of China

description: Accurate estimation of nitrogen oxide (NOx) emissions from biomass combustion is of great significance for ecological protection and climate change prediction in China. In this study, an emission factor (EF) weighting strategy considering the prior probability of the elaborate crop distribution within the fire pixels was proposed. under the assumption of Gaussian distribution of diurnal cycle of FRP (fire radiative power), we estimate the emission inventory of NOx from biomass combustion on field-level of China. To validate the assumptions of Gaussian distribution of diurnal cycle of FRP, we compared our results with the Himawari-8 satellite observations, the Pearson correlation coefficient reached 0.65. Meanwhile, in order to evaluate the emission inventory, we compared our estimation with global fire emission database (GFED), in-situ observations and ozone monitoring instrument (OMI) product, the result shows that our emission inventory with high spatiotemporal resolution agreed well with GFED database and in-situ observations in general. In conclusion, the method proposed in this study can compensate the shortage of previous studies on lacking elaborate landcovers, especially crop types while estimating the emissions, which is of great significance for countermeasure of controlling agricultural-source air pollution
DOI: 10.6084/m9.figshare.16782484.v1
title: POWV tutorial materials

description: Tutorial materials for the assessment of temporal signal dynamics.
DOI: 10.6084/m9.figshare.13352687.v3
title: Visual selective attention_ walking/standing_Singletontask

description: The folder [LabrecorderData] include the raw data of all 31 participants.The folder [StimuliData] include the information of stimuli presentation in each trial of all 31 participants.<br>All the data corresponds to the paper:'Common mechanisms of visual selective attention during standing and walking in humans'Authors: Xinyu Chen, Liyu Cao and Barbara Haendel<br>
DOI: 10.6084/m9.figshare.13352687.v2
title: Visual selective attention_ walking/standing_Singletontask

description: The folder [LabrecorderData] include the raw data of all 31 participants.The folder [StimuliData] include the information of stimuli presentation in each trial of all 31 participants.<br>All the data corresponds to the paper:'Common mechanisms of visual selective attention during standing and walking in humans'Authors: Xinyu Chen, Liyu Cao and Barbara Haendel<br>
DOI: 10.6084/m9.figshare.14666850.v1
title: Table 1. TikTok music storytelling codebook

description: Codebook
DOI: 10.6084/m9.figshare.5281099.v10
title: NBN Speedtest on Telecube HFC 50/20(Aug 17),100/40 (May 18), Kogan (100/20 Oct 2021) in Hughesdale Vic 3166

description: A speedtest of NBN speeds was run across a weekend, every two hours, from Friday 8.00pm to Monday 8.00am. NBN was running in Hughesdale, 3166 Victoria over Telecube HFC 50/20mbps NBN subscription. The NBN is the Australian National Broadband Network. HFC is the payTV network (a hybrid fibre-coaxial network) and is currently sold at speeds up to 100/40 mbps (download/upload). The test is run on a residential premise with a 50/20mbps subscription with Telecube. Where mbps is megabits per second, and 50/20 is 50mbps download and 20mbps upload.<br>Now includes updated to NBN Telecube HFC 100/40 last week. Rerun test. Friday 8.00pm to Monday 8.00am (18-20 May 2018). Using same procedure.<br><br>Speedtest is a network service to test an internet connection speed. See html5 version at http://beta.speedtest.net.<br><br>Speedtest recorded from server: 2225 Telstra Melbourne.List of servers available by running: speedtest-cli --listRecommend: run speedtest against a single server to remove server speed variability. See http://whrl.pl/ReX2rn for server variability impact on speedtest.<br>Version:8. Adding Speedtest data for a new ISP - Kogan 100/20 plan (Oct 2021). Across a weekend and testing earlier noted speedtest servers not continuously available - hence used with nearest ping. Added Kogan Plot.5. More data on 100/40 upgrade plan. File data200518new.csv added. New plot: Plotly:/50. New jpg. Plot200518.4. More data from 280817 - added every 15mins. File: data280817.csv. New plot: Plotly:/48. New jpg. Plot280817.3. Added play2.csv tidied file including parsed date/time, where DayHr = Day.Hr and DayHr2 = Day.Hr*4 *i.e. close to per cent.2. Out.csv deletes test data.1. Out.csv includes 12 lines of test data from Monash Uni LAN (speeds 200/100mbps). Now deleted in v2..<br>Recording:- upload (mbps)- download (mbps)- ping (milliseconds)and output to a csv file.<br>Code from: Github to run speedtest under python control. https://github.com/sivel/speedtest-cli (linked below).<br>Bash script executes speedtest. (Bash file attached here.)Cron controls automated execution of script and write out data to csv file. (out.csv).Further coding docs included in bash file: job.sh<br>Inspired by: https://forums.cacti.net/viewtopic.php?f=12&t=52732 <br>Cron: 5 */2 * * * cd ~/Downloads/speed/ && ./job.shRuns speedtest every two hours at five minutes past the hour.<br>Environment: MacBook Pro 2012 10.12.6 Python 2.7.10 (MacOS)<br>Data graphed (and data available) at plotly: https://plot.ly/~areff20000/46/ (linked below).<br>
DOI: 10.6084/m9.figshare.5281099.v9
title: NBN Speedtest on Telecube HFC 50/20(Aug 17),100/40 (May 18), Kogan (100/20 Oct 2021) in Hughesdale Vic 3166

description: A speedtest of NBN speeds was run across a weekend, every two hours, from Friday 8.00pm to Monday 8.00am. NBN was running in Hughesdale, 3166 Victoria over Telecube HFC 50/20mbps NBN subscription. The NBN is the Australian National Broadband Network. HFC is the payTV network (a hybrid fibre-coaxial network) and is currently sold at speeds up to 100/40 mbps (download/upload). The test is run on a residential premise with a 50/20mbps subscription with Telecube. Where mbps is megabits per second, and 50/20 is 50mbps download and 20mbps upload.<br>Now includes updated to NBN Telecube HFC 100/40 last week. Rerun test. Friday 8.00pm to Monday 8.00am (18-20 May 2018). Using same procedure.<br><br>Speedtest is a network service to test an internet connection speed. See html5 version at http://beta.speedtest.net.<br><br>Speedtest recorded from server: 2225 Telstra Melbourne.List of servers available by running: speedtest-cli --listRecommend: run speedtest against a single server to remove server speed variability. See http://whrl.pl/ReX2rn for server variability impact on speedtest.<br>Version:8. Adding Speedtest data for a new ISP - Kogan 100/20 plan (Oct 2021). Across a weekend and testing earlier noted speedtest servers not continuously available - hence used with nearest ping.5. More data on 100/40 upgrade plan. File data200518new.csv added. New plot: Plotly:/50. New jpg. Plot200518.4. More data from 280817 - added every 15mins. File: data280817.csv. New plot: Plotly:/48. New jpg. Plot280817.3. Added play2.csv tidied file including parsed date/time, where DayHr = Day.Hr and DayHr2 = Day.Hr*4 *i.e. close to per cent.2. Out.csv deletes test data.1. Out.csv includes 12 lines of test data from Monash Uni LAN (speeds 200/100mbps). Now deleted in v2..<br>Recording:- upload (mbps)- download (mbps)- ping (milliseconds)and output to a csv file.<br>Code from: Github to run speedtest under python control. https://github.com/sivel/speedtest-cli (linked below).<br>Bash script executes speedtest. (Bash file attached here.)Cron controls automated execution of script and write out data to csv file. (out.csv).Further coding docs included in bash file: job.sh<br>Inspired by: https://forums.cacti.net/viewtopic.php?f=12&t=52732 <br>Cron: 5 */2 * * * cd ~/Downloads/speed/ && ./job.shRuns speedtest every two hours at five minutes past the hour.<br>Environment: MacBook Pro 2012 10.12.6 Python 2.7.10 (MacOS)<br>Data graphed (and data available) at plotly: https://plot.ly/~areff20000/46/ (linked below).<br>
DOI: 10.6084/m9.figshare.16807258.v4
title: Abundance and distribution of different plant species at Thompson Park, Toronto

description: Meta data and data for the Biol 3250 experiment view pdf file attached
DOI: 10.6084/m9.figshare.16807258.v3
title: Abundance and distribution of different plant species at Thompson Park, Toronto

description: Meta data and data for the Biol 3250 experiment view pdf file attached
DOI: 10.6084/m9.figshare.16550910.v2
title: UHPLC Test For Ethanolic Extract of White Pyster Mushroom.docx

description: The data is original source from the my exsperimental
DOI: 10.6084/m9.figshare.3761562.v2014
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/vnn17q/usr4pp
title: Golovinomyces orontii (Castagne) Heluta F-2410.tif

description: :unav
DOI: 10.6084/m9.figshare.3761562.v2013
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.48370/ofd/vnn17q
title: Golovinomyces orontii (Castagne) Heluta, 2410

description: Fungarium Mikromycetes
DOI: 10.48370/ofd/n83eo3/f6k8h0
title: Golovinomyces orontii (Castagne) Heluta F-2405.tif

description: :unav
DOI: 10.48370/ofd/n83eo3
title: Golovinomyces orontii (Castagne) Heluta, 2405

description: Fungarium Mikromycetes
DOI: 10.48370/ofd/yuzmdk/tf3h2r
title: Golovinomyces orontii (Castagne) Heluta F-0421.tif

description: :unav
DOI: 10.48370/ofd/yuzmdk
title: Golovinomyces orontii (Castagne) Heluta, 421

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.15162795.v1
title: Supplementary File S4d – Omicsbox GFF3 annotation of protein coding genes in the scaffolds

description: This files contains information about functionally-annotated protein-coding genes for the scaffolded version of the O. australiensis Keep River genome assembly stored at NCBI in contig form: JAIFGZ000000000. Scaffolds can be obtained using the AGP file provided in this repository to order and orient the contigs. Note: contigs that could not be placed within scaffolds were annotated as individual contigs, not as ChrUn. Therefore, when using the AGP file to build scaffolds from the contigs, ChrUn lines should be excluded.
DOI: 10.48370/ofd/2ybzme/3l2tlw
title: Golovinomyces orontii (Castagne) Heluta F-0420.tif

description: :unav
DOI: 10.48370/ofd/2ybzme
title: Golovinomyces orontii (Castagne) Heluta, 420

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.15164772.v1
title: Global patterns and rates of habotat transitions across the eukaryotic tree of life

description: This repository contains supplementary data associated with the manuscript.<br>There are 8 files in this collection:<br><b>pr2.transitions.fasta.gz</b>The in house database based on the PR2 database used for taxonomic annotation of long-read environmental sequences. Fasta file.<br><b>soil.clustered.filtered.fasta.gz</b>Short-read environmental sequences from soils corresponding the V4 fragment of the 18S gene. Sequences have been clustered at 97% similarity, and low abundance sequences filtered out to be conservative (see manuscript for details). <br><b>freshwater.clustered.filtered.fasta.gz</b>Short-read environmental sequences from freshwater corresponding the V4 fragment of the 18S gene. Sequences have been clustered at 97% similarity, and low abundance sequences filtered out to be conservative (see manuscript for details).<br><b>marine_euphotic.clustered.filtered.fasta.gz</b>Short-read environmental sequences from marine euphotic layer (surface + DCM) corresponding the V4 fragment of the 18S gene. Sequences have been clustered at 97% similarity, and low abundance sequences filtered out to be conservative (see manuscript for details).<b><br>marine_aphotic.clustered.filtered.fasta.gz</b>Short-read environmental sequences from marine aphotic waters (mesopelagic + bathypelagic) corresponding the V4 fragment of the 18S gene. Sequences have been clustered at 97% similarity, and low abundance sequences filtered out to be conservative (see manuscript for details).<br><b>long_read.18S.otus.fasta.gz</b>Long read environmental sequence OTUs generated in this study and taxonomically annotated against the PR2_transitions database. Header indicates sequence ID, number of reads, sample, environment, and taxonomy. 18S sequences only.<br><b>long_read.28S.otus.fasta.gz</b>Long read environmental sequence OTUs generated in this study and taxonomically annotated against the PR2_transitions database. Header indicates sequence ID, number of reads, sample, environment, and taxonomy. 28S sequences only.<br><br>
DOI: 10.6084/m9.figshare.14124356.v3
title: Communication regarding the article “An efficient primary screening COVID-19 by serum Raman spectroscopy”

description: When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article “An efficient primary screening COVID-19 by serum Ramana spectroscopy” published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results.<br>Revision 2 contains additional experiments after the first round of peer-review.
DOI: 10.6084/m9.figshare.14124356.v2
title: Communication regarding the article “An efficient primary screening COVID-19 by serum Raman spectroscopy”

description: When performing computational modeling and machine learning experiments, it is imperative to follow a protocol that minimizes bias. In this communication, we share our concerns regarding the article “An efficient primary screening COVID-19 by serum Ramana spectroscopy” published in this journal. We consider that the authors may have inadvertently biased their results by not guaranteeing complete independence of test samples from the training data. We corroborate our point by reproducing the experiment with the available data, showing that if full independence of the test set was ensured, the reported results should be lower. We ask the authors to provide more information regarding their article, as well as making available all code used to generate their results.<br>Revision 2 contains additional experiments after the first round of peer-review.
DOI: 10.6084/m9.figshare.16826374.v3
title: An Effective Model for Evaluating The Direct Infection Risk Associated to Respiratory Events

description: Dataset and scripts for reproducing the figures included in the paper "an effective model for evaluating the direct infection risk associated to respiratory events".DOI: <br>Instructions for reproducing the figures:<br>-Figure 1 (manuscript): Provided in pdf format (vectorial).-Figure 2 (manuscript): Run the gnuplot script "figure2.py"-Figure 3 (manuscript): Run the python script "figure3.py"-Figure 4 (manuscript): Run the python script "figure4.py"-Figure 5 (manuscript): Run the python script "figure5.py"<br>-Figure 6 (manuscript): Run the python script "figure6.py"<br><br><br>Each zip file contains all the raw data required to reproduce the respective image. The resulting plots are also provided in *.png or *.pdf formats.
DOI: 10.6084/m9.figshare.16826374.v2
title: An Effective Model for Evaluating The Direct Infection Risk Associated to Respiratory Events

description: Dataset and scripts for reproducing the figures included in the paper "an effective model for evaluating the direct infection risk associated to respiratory events".DOI: <br>Instructions for reproducing the figures:<br>-Figure 2 (manuscript): Run the gnuplot script "figure2.py"-Figure 3 (manuscript): Run the python script "figure3.py"-Figure 4 (manuscript): Run the python script "figure4.py"-Figure 5 (manuscript): Run the python script "figure5.py"<br>-Figure 6 (manuscript): Run the python script "figure6.py"<br><br><br>Each zip file contains all the raw data required to reproduce the respective image. The resulting plots are also provided in *.png or *.pdf formats.
DOI: 10.6084/m9.figshare.16830736.v2
title: Additional Supplementary Tables S5-S13

description: Table S6 The different microbial pathways in ceca of HWS and LWS. Table S7 The cecal microbiota identified at the species level. Table S8 Differentially expressed genes in ceca between HWS and LWS. Table S9 The enriched functions of differentially expressed genes. Table S10 Differentially expressed genes targeted by differentially expressed miRNAs. Table S11 The distribution of differentially methylated regions in ceca of HWS and LWS. Table S12 Genes containing differentially methylated regions. Table S13 The information of the differentially methylated regions in ceca of HWS and LWS. Table S14 Hypomethylated DMRs in HWS related to CpG Island. Table S15 Genes with altered methylation and expression levels in HWS and LWS. Table S16 The association between up-regulated genes in ceca of HWS and cecal microbiota. Table S17 The association between up-regulated genes in ceca of LWS and cecal microbiota. Table S18 Data summary for 16S rRNA gene sequencing. Table S19 Data summary for mRNA sequencing. Table S20 Data summary for small RNA sequencing. Table S21 Data summary for MBD sequencing.
DOI: 10.6084/m9.figshare.16830736.v1
title: Additional Supplementary Tables S5-S13

description: Table S5A The different microbial pathways in ceca of HWS and LWS. Table S5B The cecal microbiota identified at the species level. Table S6A Differentially expressed genes in ceca between HWS and LWS. Table S6B The enriched functions of differentially expressed genes. Table S6C Differentially expressed genes targeted by differentially expressed miRNAs. Table S7A The distribution of differentially methylated regions in ceca of HWS and LWS. Table S7B Genes containing differentially methylated regions. Table S7C The information of the differentially methylated regions in ceca of HWS and LWS. Table S7D Hypomethylated DMRs in HWS related to CpG Island. Table S8 Genes with altered methylation and expression levels in HWS and LWS. Table S9A The association between up-regulated genes in ceca of HWS and cecal microbiota. Table S9B The association between up-regulated genes in ceca of LWS and cecal microbiota. Table S10 Data summary for 16S rRNA gene sequencing. Table S11 Data summary for mRNA sequencing. Table S12 Data summary for small RNA sequencing. Table S13 Data summary for MBD sequencing.
DOI: 10.48370/ofd/zyqkr6/cyajee
title: Golovinomyces sonchicola U. Braun & R.T.A. Cook F-0678.tif

description: :unav
DOI: 10.48370/ofd/zyqkr6
title: Golovinomyces sonchicola U. Braun R.T.A. Cook, 678

description: Fungarium Mikromycetes