cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21552341.v1
title: Postseismic mountain aquifer leakage drives multi-annual forearc basin recharge in the Atacama Desert

description: In response to a 7.8 M<sub>wb</sub> earthquake in June 2005 in the hyper-arid Atacama Desert, postseismic mountain aquifer leakage was triggered at several sites. By creating data-driven models for selected water level time series, previously unconsidered processes of postseismic mountain aquifer leakage are analysed. Post-event lag-time changes imply that the hydraulic conductivity of the assessed mountain aquifer doubled in response to the earthquake, initiating a multi-year period of augmented mountain-block recharge to the forearc basin. An estimate of the magnitude of mountain water releases after June 2005 suggests that released storage water exceeded the recharge provided by a 100-year rainfall event fourfold. During an observation period of 43 years two such events likely occurred in the study area. The frequency of postseismic mountain aquifer leakage, its contribution to draining eventual head decays in the Atacama Desert as well as its importance for forearc basin water budgets are discussed.
DOI: 10.6084/m9.figshare.21965735
title: Mean_daily_movement_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for mean daily movement rate analysis with fixed effects models.
DOI: 10.6084/m9.figshare.21965735.v1
title: Mean_daily_movement_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for mean daily movement rate analysis with fixed effects models.
DOI: 10.6084/m9.figshare.21965738
title: Intermtn_movement_data_all_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for intermountain movement logistic regression modeling analysis.
DOI: 10.6084/m9.figshare.21965738.v1
title: Intermtn_movement_data_all_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for intermountain movement logistic regression modeling analysis.
DOI: 10.6084/m9.figshare.21965741
title: AllDesertData_resample_females_daily_speed_stats_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for seasonal mean daily movement rate analysis (using standard linear regression modeling) for females.
DOI: 10.6084/m9.figshare.21965741.v1
title: AllDesertData_resample_females_daily_speed_stats_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for seasonal mean daily movement rate analysis (using standard linear regression modeling) for females.
DOI: 10.6084/m9.figshare.21965744
title: Intmtn_males_bioseason_counts.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for testing statistical significance of seasonal differences in intermountain movements by males using Welch's t-test.
DOI: 10.6084/m9.figshare.21965744.v1
title: Intmtn_males_bioseason_counts.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for testing statistical significance of seasonal differences in intermountain movements by males using Welch's t-test.
DOI: 10.6084/m9.figshare.21965747
title: AllDesertData_resample_adehabitat_output.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Raw data file showing results after radio-collar data were resampled to 1 location per day for all individuals by removing extraneous locations such that consecutive locations for each animal were between 20 and 28 hours apart. In cases where locations were missing and time lags exceeded 28 hours, we retained the next location to allow for the next daily time step. We calculated daily movement rates from step-lengths (i.e., Euclidean distance between 2 consecutive locations measured in meters) divided by time lags between resampled locations, and multiplied rates by 24 hours to yield meters per day (m/day); time lag values (dt) are reported in seconds, longitudinal displacement (dx), latitudinal displacement (dy), and distance (dist) are reported in meters. Output was generated using adehabitatLT in Program R (Calenge 2019; R Development Core Team 2019).
DOI: 10.6084/m9.figshare.21965747.v1
title: AllDesertData_resample_adehabitat_output.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Raw data file showing results after radio-collar data were resampled to 1 location per day for all individuals by removing extraneous locations such that consecutive locations for each animal were between 20 and 28 hours apart. In cases where locations were missing and time lags exceeded 28 hours, we retained the next location to allow for the next daily time step. We calculated daily movement rates from step-lengths (i.e., Euclidean distance between 2 consecutive locations measured in meters) divided by time lags between resampled locations, and multiplied rates by 24 hours to yield meters per day (m/day); time lag values (dt) are reported in seconds, longitudinal displacement (dx), latitudinal displacement (dy), and distance (dist) are reported in meters. Output was generated using adehabitatLT in Program R (Calenge 2019; R Development Core Team 2019).
DOI: 10.6084/m9.figshare.21965750
title: Mixed_models_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for mean daily movement rate analysis with mixed effects models.
DOI: 10.6084/m9.figshare.21965750.v1
title: Mixed_models_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for mean daily movement rate analysis with mixed effects models.
DOI: 10.6084/m9.figshare.21965753
title: Intmtn_females_bioseason_counts.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for testing statistical significance of seasonal differences in intermountain movements by females using Welch's t-test.
DOI: 10.6084/m9.figshare.21965753.v1
title: Intmtn_females_bioseason_counts.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for testing statistical significance of seasonal differences in intermountain movements by females using Welch's t-test.
DOI: 10.6084/m9.figshare.21965756.v1
title: AllDesertData_resample_males_daily_speed_stats_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for seasonal mean daily movement rate analysis (using standard linear regression modeling) for males.
DOI: 10.6084/m9.figshare.21965756
title: AllDesertData_resample_males_daily_speed_stats_input.csv from Animal movement and associated infectious disease risk in a metapopulation

description: Input file for seasonal mean daily movement rate analysis (using standard linear regression modeling) for males.
DOI: 10.6084/m9.figshare.21965759
title: Intmtn_movement_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for entire intermountain movement analysis.
DOI: 10.6084/m9.figshare.21965759.v1
title: Intmtn_movement_script.r from Animal movement and associated infectious disease risk in a metapopulation

description: R script for entire intermountain movement analysis.
DOI: 10.14457/nida.the.2016.175
title: Asean market integration: the impact on capital flow and cost of capital
DOI: 10.14457/nida.the.2018.97
title: ปัญหาทางกฎหมายเกี่ยวกับการวิเคราะห์ผลกระทบในการออกกฎหมาย (Regulatory Impact Assessment: RIA) ตามมาตรา 77 ของรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช 2560
DOI: 10.14457/nida.the.2016.176
title: Dynamic analysis on expenditure budget of tambon administrative organization
DOI: 10.14457/nida.the.2016.177
title: Co-production of public healthcare service in local communities: a case study of the family care team
DOI: 10.6084/m9.figshare.21965768
title: FIHEXO Data. from The influence of elastic ankle exoskeletons on lower limb mechanical energetics during unexpected perturbations

description: Participant average kinematic and kinetic data (N = 10) for each perturbation height (15 and 20 cm) and exoskeleton stiffness (0, 76 and 91 Nm rad-1).
DOI: 10.6084/m9.figshare.21965768.v1
title: FIHEXO Data. from The influence of elastic ankle exoskeletons on lower limb mechanical energetics during unexpected perturbations

description: Participant average kinematic and kinetic data (N = 10) for each perturbation height (15 and 20 cm) and exoskeleton stiffness (0, 76 and 91 Nm rad-1).
DOI: 10.26180/20786251.v1
title: Methods for synthesis when meta-analysis is not used: Stata code: Cumpston_et_al_2022_other_synthesis_methods 220902.do

description: This Stata .do file provides the code used to analyse the data extracted and coded from systematic reviews included in the paper: Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2022. Methods for synthesis when meta-analysis is not used: a survey of current practice in systematic reviews of public health and health systems interventions <br> <strong>Input file:</strong> Methods for synthesis when meta-analysis is not used: data file: Cumpston_et_al_2022_other_synthesis_methods.xlsx (https://doi.org/10.26180/20785396) <strong>Associated file:</strong> Methods for synthesis when meta-analysis is not used: Data dictionary (https://doi.org/10.26180/20785948) <strong>Study protocol:</strong> Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. <em>F1000Research</em> 2021, <strong>9</strong>:678. (https://doi.org/10.12688/f1000research.24469.2) <br> Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.
DOI: 10.26180/20785396.v3
title: Synthesis methods data file: Cumpston_et_al_2022_other_synthesis_methods.xlsx

description: This file includes the data extracted and coded from systematic reviews included in the paper: "Cumpston MS, Brennan SE, Ryan R, McKenzie JE. 2022. Statistical synthesis methods other than meta-analysis are commonly used, but are seldom specified: a survey of systematic reviews of interventions" <br> Associated files: 1. Synthesis methods data dictionary (https://doi.org/10.26180/20785948) 2. Synthesis methods Stata code: Cumpston_et_al_2022_other_synthesis_methods 220902.do (https://doi.org/10.26180/20786251) <strong>3. </strong>Study protocol: Cumpston MS, McKenzie JE, Thomas J and Brennan SE. The use of ‘PICO for synthesis’ and methods for synthesis without meta-analysis: protocol for a survey of current practice in systematic reviews of health interventions. <em>F1000Research</em> 2021, <strong>9</strong>:678. (https://doi.org/10.12688/f1000research.24469.2) <br> <br> Note: Naming convention of the variables. The naming convention for the variables links to the data dictionary. The character prefix identifies the section of the data_directory (e.g. variables names with the prefix 'Chars' are from the 'CHARACTERISTICS' section). The number of the variable reflects the item number in the data dictionary, except that the first digit is removed because this is captured by the character prefix. For example, Chars_2 is item number 1.2 under the 'CHARACTERISTICS' section of the data dictionary.
DOI: 10.6084/m9.figshare.21965651
title: 488 carp 250K SNP array

description: <strong>488 carp 250K SNP array data of </strong> Cyprinus carpio Linnaeus, 1758
DOI: 10.5281/zenodo.7574771
title: Contribution function for the GOLD disk temperature observations

description: Contribution function for the GOLD disk temperature observations. These are valid for low-solar activity condition. The contents of netCDF files are as follows: dimensions:<br> n_alts = 91 ;<br> n_szas = 6 ;<br> variables:<br> float sol_zen(n_szas) ;<br> sol_zen:units = "degree" ;<br> sol_zen:long_name = "Solar Zenith Angle" ;<br> float altitude(n_alts) ;<br> altitude:units = "km" ;<br> altitude:long_name = "Geometric altitude" ;<br> float CF(n_szas, n_alts) ;<br> CF:units = "unitless" ;<br> CF:long_name = "Normalized Contribution Function" ; // global attributes:<br> :description = "Contribution Function for the GOLD Tdisk at low solar activity condition; Courtesy: J. S. Evans, CPI" ;<br> :history = "Created 2023-01-26 17:23:54.900047" ;
DOI: 10.5281/zenodo.7574770
title: Contribution function for the GOLD disk temperature observations

description: Contribution function for the GOLD disk temperature observations. These are valid for low-solar activity condition. The contents of netCDF files are as follows: dimensions:<br> n_alts = 91 ;<br> n_szas = 6 ;<br> variables:<br> float sol_zen(n_szas) ;<br> sol_zen:units = "degree" ;<br> sol_zen:long_name = "Solar Zenith Angle" ;<br> float altitude(n_alts) ;<br> altitude:units = "km" ;<br> altitude:long_name = "Geometric altitude" ;<br> float CF(n_szas, n_alts) ;<br> CF:units = "unitless" ;<br> CF:long_name = "Normalized Contribution Function" ; // global attributes:<br> :description = "Contribution Function for the GOLD Tdisk at low solar activity condition; Courtesy: J. S. Evans, CPI" ;<br> :history = "Created 2023-01-26 17:23:54.900047" ;
DOI: 10.6084/m9.figshare.21965792
title: Data set_artists_pwt.xlsx

description: Decent work for artists during COVID-19: job insecurity, economic constraints, calling, and career adaptability
DOI: 10.6084/m9.figshare.21965792.v1
title: Data set_artists_pwt.xlsx

description: Decent work for artists during COVID-19: job insecurity, economic constraints, calling, and career adaptability
DOI: 10.25582/data.2020-01.1911955/1916627
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-04.1974353/1916628
title: Data for EMSL Project 51084 from April 2020
DOI: 10.25582/data.2020-12.2100025/1916629
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-12.2100120/1916630
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-01.1911952/1916631
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2021-11.2384875/1916632
title: Data for EMSL Project 51084 from November 2021
DOI: 10.25582/data.2020-01.1911956/1916633
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-02.1947489/1916634
title: Data for EMSL Project 51084 from February 2020
DOI: 10.25582/data.2020-01.1915480/1916635
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-01.1911951/1916636
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-12.2101209/1916637
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-04.1973545/1916638
title: Data for EMSL Project 51084 from April 2020
DOI: 10.25582/data.2019-12.1897752/1916639
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2020-02.1944404/1916640
title: Data for EMSL Project 51084 from February 2020
DOI: 10.25582/data.2021-11.2388125/1916641
title: Data for EMSL Project 51084 from November 2021
DOI: 10.25582/data.2020-02.1943909/1916642
title: Data for EMSL Project 51084 from February 2020
DOI: 10.25582/data.2020-12.2100846/1916643
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-03.1962990/1916644
title: Data for EMSL Project 51084 from March 2020
DOI: 10.25582/data.2021-05.2224083/1916645
title: Data for EMSL Project 51084 from May 2021
DOI: 10.25582/data.2020-03.1963511/1916646
title: Data for EMSL Project 51084 from March 2020
DOI: 10.25582/data.2021-02.2142947/1916647
title: Data for EMSL Project 51084 from February 2021
DOI: 10.25582/data.2020-01.1915347/1916648
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-01.1911979/1916649
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-04.1973584/1916650
title: Data for EMSL Project 51084 from April 2020
DOI: 10.25582/data.2020-02.1955146/1916651
title: Data for EMSL Project 51084 from February 2020
DOI: 10.25582/data.2021-01.2137802/1916652
title: Data for EMSL Project 51084 from January 2021
DOI: 10.25582/data.2019-12.1898689/1916653
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2019-12.1900520/1916654
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2021-05.2224639/1916655
title: Data for EMSL Project 51084 from May 2021
DOI: 10.25582/data.2020-12.2101824/1916656
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-12.2101071/1916657
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2021-02.2147078/1916658
title: Data for EMSL Project 51084 from February 2021
DOI: 10.25582/data.2021-11.2384925/1916659
title: Data for EMSL Project 51084 from November 2021
DOI: 10.25582/data.2019-12.1900623/1916660
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2019-12.1897348/1916661
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2021-11.2386848/1916662
title: Data for EMSL Project 51084 from November 2021
DOI: 10.25582/data.2021-01.2137803/1916663
title: Data for EMSL Project 51084 from January 2021
DOI: 10.25582/data.2020-01.1915455/1916664
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2019-12.1900880/1916665
title: Data for EMSL Project 51084 from December 2019
DOI: 10.25582/data.2020-01.1912174/1916666
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-01.1912060/1916667
title: Data for EMSL Project 51084 from January 2020
DOI: 10.25582/data.2020-12.2100018/1916668
title: Data for EMSL Project 51084 from December 2020
DOI: 10.25582/data.2020-12.2116534/1916669
title: Data for EMSL Project 51084 from December 2020
DOI: 10.26180/21965777
title: Australian Youth Resilience and Help-Seeking 2021

description: This dataset contains responses from Australian youth on the 14-item Resilience Scale (RS-14), General Help Seeking Questionnaire (GHSQ) and Actual Help Seeking Questionnaire (AHSQ) from July to September 2021.
DOI: 10.26180/21965777.v1
title: Australian Youth Resilience and Help-Seeking 2021

description: This dataset contains responses from Australian youth on the 14-item Resilience Scale (RS-14), General Help Seeking Questionnaire (GHSQ) and Actual Help Seeking Questionnaire (AHSQ) from July to September 2021.
DOI: 10.25582/data.2021-02.2142918/1916670
title: Data for EMSL Project 51084 from February 2021
DOI: 10.25582/data.2021-01.2138165/1916671
title: Data for EMSL Project 51084 from January 2021
DOI: 10.25582/data.2019-08.1831665/1916672
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1834251/1916673
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1841516/1916674
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1836072/1916675
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1845473/1916676
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1839666/1916677
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1832095/1916678
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1835135/1916679
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1839196/1916680
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1841541/1916681
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-07.1819226/1916682
title: Data for EMSL Project 50717 from July 2019
DOI: 10.25582/data.2019-09.1850250/1916683
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1828038/1916684
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1845981/1916685
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1835092/1916686
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1839706/1916687
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1831370/1916688
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-07.1809489/1916689
title: Data for EMSL Project 50717 from July 2019
DOI: 10.25582/data.2019-08.1831701/1916690
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1833097/1916691
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1845862/1916692
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1846120/1916693
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-07.1809505/1916694
title: Data for EMSL Project 50717 from July 2019
DOI: 10.25582/data.2019-08.1834785/1916695
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1837194/1916696
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1836954/1916697
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1832108/1916698
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1840158/1916699
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1837475/1916700
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-07.1809487/1916701
title: Data for EMSL Project 50717 from July 2019
DOI: 10.25582/data.2019-08.1835327/1916702
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-06.1793299/1916703
title: Data for EMSL Project 50717 from June 2019
DOI: 10.25582/data.2019-09.1838806/1916704
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1837160/1916705
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1832129/1916706
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1849595/1916707
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1845387/1916708
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1839486/1916709
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1837541/1916710
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1849052/1916711
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1836403/1916712
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1846086/1916713
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-09.1841868/1916714
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-07.1819143/1916715
title: Data for EMSL Project 50717 from July 2019
DOI: 10.25582/data.2019-09.1840980/1916716
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1827684/1916717
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1836458/1916718
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1825624/1916719
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-06.1793297/1916720
title: Data for EMSL Project 50717 from June 2019
DOI: 10.25582/data.2019-08.1836989/1916721
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-08.1832338/1916722
title: Data for EMSL Project 50717 from August 2019
DOI: 10.25582/data.2019-09.1843745/1916723
title: Data for EMSL Project 50717 from September 2019
DOI: 10.25582/data.2019-08.1832091/1916724
title: Data for EMSL Project 50717 from August 2019