cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12695400
title: Additional file 1 of The use of UK primary care databases in health technology assessments carried out by the National Institute for health and care excellence (NICE)

description: Additional file 1. Breakdown of disease areas where primary care databases are used in NICE technology appraisals.
DOI: 10.6084/m9.figshare.12695403.v1
title: Additional file 2 of The use of UK primary care databases in health technology assessments carried out by the National Institute for health and care excellence (NICE)

description: Additional file 2. A list of all the NICE technology appraisals reference numbers that were included in the analysis.
DOI: 10.6084/m9.figshare.12695400.v1
title: Additional file 1 of The use of UK primary care databases in health technology assessments carried out by the National Institute for health and care excellence (NICE)

description: Additional file 1. Breakdown of disease areas where primary care databases are used in NICE technology appraisals.
DOI: 10.6084/m9.figshare.12694598
title: JVASP-3966 to JVASP-6304

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12694601
title: JVASP-1843 to JVASP-3963

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12694577
title: JVASP-6307 to JVASP-8118

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12694301
title: JVASP-5 to JVASP-1840

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12694601.v1
title: JVASP-1843 to JVASP-3963

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12694043
title: pnas2005496117_data_KI-M.xlsx

description: The source data for "Tuning the circadian period of cyanobacteria up to 6.6 days by the single amino acid substitutions in KaiC" (DOI :10.1073/pnas.2005496117).
DOI: 10.6084/m9.figshare.12694088
title: Exomes analysis

description: Exome sequencing
DOI: 10.6084/m9.figshare.12694088.v1
title: Exomes analysis

description: Exome sequencing
DOI: 10.6084/m9.figshare.12693461
title: Pediatric intensive care unit admissions for COVID-19: insights using state-level data

description: The state level data contains 13 different factors from population density, comorbid conditions and social distancing score.
DOI: 10.6084/m9.figshare.12691856
title: Working nights and lower leisure-time physical activity associate with chronic pain in Southern African truck drivers

description: <b>Abstract</b><b> </b><b>Background</b>Globally the trucking industry employs millions of people. Recently the prevalence of chronic pain in Southern African truck drivers was reported at 10%. We investigated factors associated with chronic pain in truck drivers including mental health, physical activity, and sleep, as no study has done so. <b>Methods</b>Southern African truck drivers were recruited at truck stops in Gauteng and Free State Provinces, South Africa (n=614). Chronic pain was defined as pain present for at least the last three months. Depressive symptoms were assessed with the Patient Health Questionnaire-9, post-traumatic stress disorder with the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), exposure to traumatic events with the Life Events Checklist-5 (LEC-5) and daytime sleepiness with the Epworth Sleepiness Scale. Sleep quality was measured on a four-point Likert scale. Leisure-time physical activity was measured using the Godin-Shephard leisure-time physical activity questionnaire. Associations between these factors, demographic factors and chronic pain were investigated. <b>Results</b>Six hundred and fourteen male truck drivers were recruited. Multivariate analysis showed that working ≥ 2 nights/week (OR=2.68, 95% CI=1.55-4.68) was associated with chronic pain and physical activity was protective (OR=0.97, 95% CI 0.95-0.98). In an exploratory analysis, greater depressive symptoms (p=0.004), daytime sleepiness (p=0.01) and worse sleep quality (p=0.001) was associated with working ≥ 2 nights/week. Lower leisure-time physical activity was associated with worse sleep quality (p=0.006), but not daytime sleepiness or depressive symptoms (p&gt;0.05). <b>Conclusions</b>There is a clear relationship between working nights and activity levels, and chronic pain, sleep quality, and depression in truck drivers.
DOI: 10.6084/m9.figshare.12691823
title: SLA_5May2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 May 2015.
DOI: 10.6084/m9.figshare.12691832
title: propressing dataset

description: propressing dataset
DOI: 10.6084/m9.figshare.12691823.v1
title: SLA_5May2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 May 2015.
DOI: 10.6084/m9.figshare.12691820
title: SLA_5Apr2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 April 2015.
DOI: 10.6084/m9.figshare.12691832.v2
title: propressing dataset

description: propressing dataset
DOI: 10.6084/m9.figshare.12691817
title: SLA_5Mar2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 March 2015.
DOI: 10.6084/m9.figshare.12691820.v1
title: SLA_5Apr2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 April 2015.
DOI: 10.6084/m9.figshare.12691832.v1
title: propressing dataset

description: propressing dataset
DOI: 10.6084/m9.figshare.12691814
title: SLA_5Feb2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 Feb 2015.
DOI: 10.6084/m9.figshare.12691817.v1
title: SLA_5Mar2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 March 2015.
DOI: 10.6084/m9.figshare.12691757
title: SLA_5Jan2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 Jan 2015.
DOI: 10.6084/m9.figshare.12691757.v1
title: SLA_5Jan2015

description: Gridded SLA data from cds.climate.copernicus.eu for 5 Jan 2015.
DOI: 10.6084/m9.figshare.12691727
title: dataset2013143.xls

description: dataset of waist to height ratio and type 2 diabetes
DOI: 10.6084/m9.figshare.12691655
title: SLA_5Dec2014

description: Gridded SLA data from https://cds.climate.copernicus.eu from 5 Dec 2014.
DOI: 10.6084/m9.figshare.12691727.v1
title: dataset2013143.xls

description: dataset of waist to height ratio and type 2 diabetes
DOI: 10.6084/m9.figshare.12691655.v1
title: SLA_5Dec2014

description: Gridded SLA data from https://cds.climate.copernicus.eu from 5 Dec 2014.
DOI: 10.6084/m9.figshare.12690152
title: GIS based watershed’s morphometric parameters analysis for sustainable hydrological modeling: A case study of Barind region of Bangladesh

description: Here is attached DEM image and analyzed data.
DOI: 10.6084/m9.figshare.12689882
title: Neues Textdokument - Kopie (4) - Kopie.txt

description: super nice<br>
DOI: 10.6084/m9.figshare.12689882.v1
title: Neues Textdokument - Kopie (4) - Kopie.txt

description: super nice<br>
DOI: 10.5445/ir/1000122856
title: Research Data for publication 'Electrodeposition of chitosan enables synthesis of copper/carbon composites for H2O2 sensing'
DOI: 10.6084/m9.figshare.12689615
title: Additional data.xlsx

description: Linked genotype and phenotype data of fine-mapping
DOI: 10.6084/m9.figshare.12689603
title: Fall Spring HSC data

description: Data set for Fall/Spring horseshoe crab comparisons<br>
DOI: 10.6084/m9.figshare.12689603.v1
title: Fall Spring HSC data

description: Data set for Fall/Spring horseshoe crab comparisons<br>
DOI: 10.6084/m9.figshare.12689258
title: Estimating the risk of species interaction loss in mutualistic communities

description: S1 Data. Data underlying Fig 2. x = vulnerability values; y = importance values; x_pred = best-fit line vulnerability values; y_pred = best-fit line importance values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval.<br>S2 Data. Data underlying S1 Figure. x = vulnerability values; y = importance values; x_pred = best-fit line vulnerability values; y_pred = best-fit line importance values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval.<br><br>S3 Data. Data underlying S2 Figure. x = x axis values; y = y axis values; x_pred = best-fit line x values; y_pred = best-fit line y values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval; panel = which panel of S2 Figure the data correspond to.<br>S4 Data. Data underlying S6 Figure. vulnerability = the vulnerability values; included = whether the link was included in the analysis or was excluded (see Methods). <br>S5 Data. Data underlying Figure A in S4 Analysis.<br>S6 Data. Data underlying Figure B in S4 Analysis. Estimates = slope of relationship between feasibility contribution and generalisation; rhos = rho; deltas = delta; gthreshs = average mutualistic benefit.<br>S7 Data. Data underlying Figure C in S4 Analysis. Estimates = slope of relationship between feasibility contribution and generalisation; rhos = rho; deltas = delta; gthreshs = average mutualistic benefit.<br>S8 Data. Data underlying Figure A in S5 Analysis. Measure = type of abundance used; probability = abundance-driven interaction probability; frequency = observed interaction frequency. <br>S9 Data. Data underlying Fig 3, and S4 Figure. taxonomic_level = taxonomic level; property = aizen_freq (frequency), aizen_gen (generalisation), vulnerability or importance (feasibility contribution).<br>S10 Data. Data underlying S3 Figure, and S5 Figure. taxonomic_level = taxonomic level; property = aizen_freq (frequency), aizen_gen (generalisation), vulnerability or importance (feasibility contribution).
DOI: 10.6084/m9.figshare.12689270.v1
title: The genome sequences of Cerasus humilis polished with Illumina short reads

description: This genome was assembled using Falcon and then chromosome-level assembled with Hi-C data. After assembly, NextPolish was used to polish the genome.<br>
DOI: 10.6084/m9.figshare.12689258.v1
title: Estimating the risk of species interaction loss in mutualistic communities

description: S1 Data. Data underlying Fig 2. x = vulnerability values; y = importance values; x_pred = best-fit line vulnerability values; y_pred = best-fit line importance values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval.<br>S2 Data. Data underlying S1 Figure. x = vulnerability values; y = importance values; x_pred = best-fit line vulnerability values; y_pred = best-fit line importance values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval.<br><br>S3 Data. Data underlying S2 Figure. x = x axis values; y = y axis values; x_pred = best-fit line x values; y_pred = best-fit line y values; plo = best-fit line lower confidence interval; phi = best-fit line upper confidence interval; panel = which panel of S2 Figure the data correspond to.<br>S4 Data. Data underlying S6 Figure. vulnerability = the vulnerability values; included = whether the link was included in the analysis or was excluded (see Methods). <br>S5 Data. Data underlying Figure A in S4 Analysis.<br>S6 Data. Data underlying Figure B in S4 Analysis. Estimates = slope of relationship between feasibility contribution and generalisation; rhos = rho; deltas = delta; gthreshs = average mutualistic benefit.<br>S7 Data. Data underlying Figure C in S4 Analysis. Estimates = slope of relationship between feasibility contribution and generalisation; rhos = rho; deltas = delta; gthreshs = average mutualistic benefit.<br>S8 Data. Data underlying Figure A in S5 Analysis. Measure = type of abundance used; probability = abundance-driven interaction probability; frequency = observed interaction frequency. <br>S9 Data. Data underlying Fig 3, and S4 Figure. taxonomic_level = taxonomic level; property = aizen_freq (frequency), aizen_gen (generalisation), vulnerability or importance (feasibility contribution).<br>S10 Data. Data underlying S3 Figure, and S5 Figure. taxonomic_level = taxonomic level; property = aizen_freq (frequency), aizen_gen (generalisation), vulnerability or importance (feasibility contribution).
DOI: 10.6084/m9.figshare.12689042
title: Short read sequence data

description: Short read sequence data for VRE paper
DOI: 10.6084/m9.figshare.12688478
title: Data and code "Church Voting Recommendations, Voter Preferences, and Political Decisions"

description: This fileset contains data and code for the paper "Church Voting Recommendations, Voter Preferences, and Political Decisions", Journal for the Scientific Study of Religion, DOI 10.1111/jssr.12669<br><br>Abstract: In Switzerland, two key church institutions—the Conference of Swiss Bishops and the Federation of Protestant Churches—make public recommendations on how to vote for certain referenda. We leverage this situation to directly measure religious organizations’ power to reflect and shape human decision making. That is, using a direct measure of voters’ commitment to their religious institution, we analyze whether they are more likely to vote in line with this organization's voting recommendation. We find that these recommendations do indeed wield influence, implying that even in a secularized world, religion plays a crucial role in voting decisions. Our results hold for both aggregate and individual‐level voting decisions.
DOI: 10.6084/m9.figshare.12688334
title: Vegetation survey in the steppic rangelands of Algeria.xlsx

description: A multi-site survey was conducted during the period of peak growth of vegetation (late April-early May) in 2014. We selected five homogeneous sites in grazing-excluded steppes and another five in freely grazed steppes. Within each site, three 200-m linear transects (30 transects in total) were carried out. Plant abundance and richness within each transect were estimated using the Point-Intercept Method, which consists of registering all species touching the nail every 20-cm intervals. The abundance of each species in each transect was estimated from the number of individuals recorded along the same transect
DOI: 10.6084/m9.figshare.12688334.v1
title: Vegetation survey in the steppic rangelands of Algeria.xlsx

description: A multi-site survey was conducted during the period of peak growth of vegetation (late April-early May) in 2014. We selected five homogeneous sites in grazing-excluded steppes and another five in freely grazed steppes. Within each site, three 200-m linear transects (30 transects in total) were carried out. Plant abundance and richness within each transect were estimated using the Point-Intercept Method, which consists of registering all species touching the nail every 20-cm intervals. The abundance of each species in each transect was estimated from the number of individuals recorded along the same transect
DOI: 10.6084/m9.figshare.12687908
title: Maximum constraint on the response of terrestrial productivity to precipitation variability

description: Code and datasets used in the article "Maximum constraint on the response of terrestrial productivity to precipitation variability"<br>
DOI: 10.6084/m9.figshare.12687839
title: S5 Data Extraction Table Turmeric VRolfe 21July2020.xlsx

description: Data part of Turmeric Meta-Review dataset and includes assessment of Overlap of Primary Studies and GRADE Analysis on the quality of evidence.
DOI: 10.6084/m9.figshare.12687839.v1
title: S5 Data Extraction Table Turmeric VRolfe 21July2020.xlsx

description: Data part of Turmeric Meta-Review dataset and includes assessment of Overlap of Primary Studies and GRADE Analysis on the quality of evidence.
DOI: 10.6084/m9.figshare.12687821
title: Lake/Sample IDs present in the all_samples and all_features datasets

description: Lists of sample/lake IDs that indicate the composition of the all_samples and all_features datasets<br>
DOI: 10.6084/m9.figshare.12687821.v1
title: Lake/Sample IDs present in the all_samples and all_features datasets

description: Lists of sample/lake IDs that indicate the composition of the all_samples and all_features datasets<br>
DOI: 10.6084/m9.figshare.12687794
title: Bioindicators for physico-chemical lake parameters

description: A list of bioindicators identified using the IndVal method with statistical information first described in Dufrêne & Legendre, 1997 (10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2) over seven levels of taxonomy and for a set of physico-chemical parameters. For bioindicator OTUs, a second table also includes taxonomic annotation.<br>
DOI: 10.6084/m9.figshare.12687797
title: Traffic Congestion Index (TCI) of New Delhi, Mumbai, Jakarta, Bogota, Lima, and Stockholm

description: The Traffic Congestion Index (TCI) data between 1 January 2019 to 30 June 2020, for New Delhi, Mumbai, Jakarta, Bogota, Lima, and Stockholm. The data were obtained from http://www.trafficindex.org.<br>The website was developed by Tudor Bastea.<br>
DOI: 10.6084/m9.figshare.12687794.v1
title: Bioindicators for physico-chemical lake parameters

description: A list of bioindicators identified using the IndVal method with statistical information first described in Dufrêne & Legendre, 1997 (10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2) over seven levels of taxonomy and for a set of physico-chemical parameters. For bioindicator OTUs, a second table also includes taxonomic annotation.<br>
DOI: 10.6084/m9.figshare.12687695
title: Hourly PM2.5 concentrations for 12 cities (Beijing, Shanghai, New Delhi, Mumbai, Kolkata, Dhaka, Jakarta, Ho Chi Minh City, Bogota, Lima, Ulaanbaatar, and Abu Dhabi)

description: Hourly PM2.5 concentrations for 12 cities (Beijing, Shanghai, New Delhi, Mumbai, Kolkata, Dhaka, Jakarta, Ho Chi Minh City, Bogota, Lima, Ulaanbaatar, and Abu Dhabi) between 1 January 2019 and 29 June 2020. The data were obtained from AirNow DOS website (https://www.airnow.gov/international/us-embassies-and-consulates/).
DOI: 10.6084/m9.figshare.12687722
title: Datos right-wing populism in Andalucia (Spain).xlsx

description: These are the data used in the paper entitled "What are the factors behind the irruption of extreme right-wing populism in the Andalusian 2018 election? Modeling the results"<br>
DOI: 10.6084/m9.figshare.12687755
title: Evaluations of framework for the quantification of shared information in microbial lake ecology

description: This dataset contains evaluations of different models that were used to quantify the information shared or co-variation between the microbiome and a set of physico-chemical parameters. Input parameters were filtered using either the fast correlation-based filter (FCBF) presented in Yu & Liu, 2008 (DOI:10.1109/ISCIS.2008.4717949) or the IndVal method first described in Dufrêne & Legendre, 1997 (10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2).<br>
DOI: 10.6084/m9.figshare.12687687
title: Data from Nitrate supply and uptake in the Atlantic Arctic sea ice zone: seasonal cycle, mechanisms and drivers

description: Zip file containing all data used in this study. Time-series nitrate, temperature, salinity, density, PAR and fluorescence data from 21 m at each mooring. Full-depth time-series temperature and salinity data from each mooring. These data are also publicly available through the British Oceanographic Data Centre (BODC).
DOI: 10.6084/m9.figshare.12687755.v1
title: Evaluations of framework for the quantification of shared information in microbial lake ecology

description: This dataset contains evaluations of different models that were used to quantify the information shared or co-variation between the microbiome and a set of physico-chemical parameters. Input parameters were filtered using either the fast correlation-based filter (FCBF) presented in Yu & Liu, 2008 (DOI:10.1109/ISCIS.2008.4717949) or the IndVal method first described in Dufrêne & Legendre, 1997 (10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2).<br>
DOI: 10.6084/m9.figshare.12687687.v1
title: Data from Nitrate supply and uptake in the Atlantic Arctic sea ice zone: seasonal cycle, mechanisms and drivers

description: Zip file containing all data used in this study. Time-series nitrate, temperature, salinity, density, PAR and fluorescence data from 21 m at each mooring. Full-depth time-series temperature and salinity data from each mooring. These data are also publicly available through the British Oceanographic Data Centre (BODC).
DOI: 10.6084/m9.figshare.12687678
title: Supplementary information datasets from Transformation of organic matter in a Barents Sea sediment profile: coupled geochemical and microbiological processes

description: Datasets utilised within this study
DOI: 10.6084/m9.figshare.12687695.v2
title: Hourly PM2.5 concentrations for 12 cities (Beijing, Shanghai, New Delhi, Mumbai, Kolkata, Dhaka, Jakarta, Ho Chi Minh City, Bogota, Lima, Ulaanbaatar, and Abu Dhabi)

description: Hourly PM2.5 concentrations for 12 cities (Beijing, Shanghai, New Delhi, Mumbai, Kolkata, Dhaka, Jakarta, Ho Chi Minh City, Bogota, Lima, Ulaanbaatar, and Abu Dhabi) between 1 January 2019 and 29 June 2020. The data were obtained from AirNow DOS website (https://www.airnow.gov/international/us-embassies-and-consulates/).
DOI: 10.6084/m9.figshare.12687695.v1
title: PM2.5_data.zip

description: Hourly PM2.5 concentrations for 12 cities (Beijing, Shanghai, New Delhi, Mumbai, Kolkata, Dhaka, Jakarta, Ho Chi Minh City, Bogota, Lima, Ulaanbaatar, and Abu Dhabi) between 1 January 2019 and 29 June 2020. The data were obtained from AirNow DOS website (https://www.airnow.gov/international/us-embassies-and-consulates/).
DOI: 10.6084/m9.figshare.12687671
title: Latent inhibition in honeybees

description: Data set used to generate the figures in the manuscript entitled "Early olfactory processing but not the gustatory processing is affected by the selection of cognitive phenotypes in honey bee genetic lines"
DOI: 10.6084/m9.figshare.12687678.v1
title: Supplementary information datasets from Transformation of organic matter in a Barents Sea sediment profile: coupled geochemical and microbiological processes

description: Datasets utilised within this study
DOI: 10.25388/nwu.12687566
title: Afrikaanse werkwoorde met verledetydvorme [Afrikaans verbs with past-tense forms]

description: Version 1.0 comprises 5,523 Afrikaans verbs with associated past tense forms (tab separated). Verbs were sourced from various sources, mainly from CTexT’s Afrikaans lemmatisation data, and supplemented with data from the Afrkaans/Dutch-Dutch/Afrikaans Dictionary (ANNA). Past tense forms were generated and manually verified.<br><br>Copyright (c) 2015 Centre for Text Technology (CTexT), North-West University, South Africa.Contributor: Martin Puttkammer; Gerhard B van HuyssteenContributor: Gridline, The Netherlands. Licence: Creative Commons Attribution 2.5 South Africa LicenseURL: http://creativecommons.org/licenses/by/2.5/za/ Attribute work to: Puttkammer, MJ & Van Huyssteen, GB. 2015. Afrikaanse werkwoorde met verledetydvorme. Potchefstroom: Centre for Text Technology (CTexT), North-West University.Attribute work to URL: http://www.nwu.ac.za/ctext
DOI: 10.6084/m9.figshare.12687671.v1
title: Latent inhibition in honeybees

description: Data set used to generate the figures in the manuscript entitled "Early olfactory processing but not the gustatory processing is affected by the selection of cognitive phenotypes in honey bee genetic lines"
DOI: 10.25388/nwu.12687566.v1
title: Afrikaanse werkwoorde met verledetydvorme [Afrikaans verbs with past-tense forms]

description: Version 1.0 comprises 5,523 Afrikaans verbs with associated past tense forms (tab separated). Verbs were sourced from various sources, mainly from CTexT’s Afrikaans lemmatisation data, and supplemented with data from the Afrkaans/Dutch-Dutch/Afrikaans Dictionary (ANNA). Past tense forms were generated and manually verified.<br><br>Copyright (c) 2015 Centre for Text Technology (CTexT), North-West University, South Africa.Contributor: Martin Puttkammer; Gerhard B van HuyssteenContributor: Gridline, The Netherlands. Licence: Creative Commons Attribution 2.5 South Africa LicenseURL: http://creativecommons.org/licenses/by/2.5/za/ Attribute work to: Puttkammer, MJ & Van Huyssteen, GB. 2015. Afrikaanse werkwoorde met verledetydvorme. Potchefstroom: Centre for Text Technology (CTexT), North-West University.Attribute work to URL: http://www.nwu.ac.za/ctext