cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22008/fk2/3tsbf0/9f2pwi
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/lniqje
title: payload_decoder.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/0ys6p6
title: get_l0tx.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/k1w40y
title: tx.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/va3typ
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/krbsa0
title: get_msg.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/zskafw
title: rest_api_client.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/lsizas
title: uid_handler.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/mv6vik
title: base_mail_client.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/o1hes7
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/c0mbrn
title: .gitignore

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/r6rpfk
title: imap_client.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/uypeom
title: CITATION.cff

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/zkm8ye
title: README.md

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/02gqnd
title: LICENSE.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/4c50yy
title: pyproject.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/b4bcf5
title: environment.yml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/48mcqu
title: .readthedocs.yaml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/9u7krl
title: MANIFEST.in

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/9lngye
title: paper.md

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/fw2hqu
title: paper.bib

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/8kbgoj
title: requirements.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/xc7k7w
title: technical_process.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/yvth9b
title: modules.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/bribdl
title: guide_user.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ghsxxc
title: guide_developer.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/xlmog5
title: make.bat

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/exzkxt
title: conf.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/tcmsos
title: technical_data.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/90r3xx
title: index.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/iiyc1f
title: install.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ptjqxy
title: Makefile

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/15x4wn
title: utilities.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/wbnfu9
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/uf3cvn
title: test_get_l2.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/3el5uy
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/eyehse
title: test_process.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ef1rpd
title: test_payload_formats.csv

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/whpbe9
title: test_config2_raw.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/lfeojz
title: test_config2_tx.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.15468/dl.s6ftad
title: Occurrence Download

description: A dataset containing 1029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "TaxonKey is Trigona"
 ]
}

 The dataset includes 1029 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007381-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/3tsbf0/23kaya
title: test_raw1.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/iv9jwt
title: test_raw_SlimTableMem1.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/xrb8b6
title: test_raw_transmitted2.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ecouir
title: test_payload_types.csv

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/dfnadi
title: test_config1_tx.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/vsni5p
title: test_email

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/cjw9pp
title: test_config1_raw.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/5jvh8x
title: test_raw_transmitted1.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/hydayu
title: test_raw_DataTable2.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/vjqya4
title: TEST1.toml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.15468/dl.jcpgcm
title: Occurrence Download

description: A dataset containing 7953 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is one of (USGS Nonindigenous Aquatic Species database, USGS Great Lakes Science Center Research Vessel Catch Information System Trawl, University of Michigan Museum of Zoology, Division of Fishes, Ohio State University Fish Division (OSUM), Field Museum of Natural History (Zoology) Fish Collection, Multistate Aquatic Resources Information System - MARIS - Fish, Vertebrate Zoology Division - Ichthyology, Yale Peabody Museum, INSDC Sequences, International Barcode of Life project (iBOL))",
 "TaxonKey is Neogobius melanostomus"
 ]
}

 The dataset includes 7953 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007382-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/3tsbf0/9ho9hs
title: test_station_config.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/xembi9
title: test_value_clippping.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/escr88
title: test_percentile.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/kvbj4x
title: tx.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.5281/zenodo.16947690
title: Experimental Data Supporting 'Consequences of Melt Memory and Thermal Degradation for Poly(3-hydroxybutyrate-co-4-hydroxybutyrate) Crystallization'

description: Dataset generated for the manuscript 'Consequences of Melt Memory and Thermal Degradation for Poly(3-hydroxybutyrate-<i>co<\/i>-4-hydroxybutyrate) Crystallization'  (https://doi.org/10.1021/acs.macromol.5c02581).
DOI: 10.5281/zenodo.16947691
title: Experimental Data Supporting 'Consequences of Melt Memory and Thermal Degradation for Poly(3-hydroxybutyrate-co-4-hydroxybutyrate) Crystallization'

description: Dataset generated for the manuscript 'Consequences of Melt Memory and Thermal Degradation for Poly(3-hydroxybutyrate-<i>co<\/i>-4-hydroxybutyrate) Crystallization'  (https://doi.org/10.1021/acs.macromol.5c02581).
DOI: 10.6084/m9.figshare.31136519
title: Psychometric properties of temporomandibular disorder-specific patient-reported outcome measures: A systematic review

description: Patient-Reported Outcome Measures (PROMs) serve as critical tools for quantifying patient’s subjective experience. The aim of this study was to systematically identify and synthesize the psychometric properties of TMD-specific PROMs. A systematic search was conducted until September 15, 2025. Studies reporting on the development, validation, or cross-cultural adaptation of TMD-specific PROMs were included. Data on psychometric properties were narratively synthesized. The review of 91 studies yielded 43 PROMs. The Fonseca Anamnestic Index was the most extensively validated screening tool. Jaw Functional Limitation Scale, Tampa Scale for Kinesiophobia, and the Oral Health Impact Profile for TMD demonstrated excellent reliability (Cronbach’s α often &gt;0.85-0.95) and cross-cultural validity. Test-retest reliability was strong for most tools (ICCs commonly &gt;0.80), and convergent validity showed moderate-to-strong correlations with established measures. Numerous validated PROMs for TMDs are available, however future work should focus on developing integrated tools with extensive validation.
DOI: 10.6084/m9.figshare.31136519.v1
title: Psychometric properties of temporomandibular disorder-specific patient-reported outcome measures: A systematic review

description: Patient-Reported Outcome Measures (PROMs) serve as critical tools for quantifying patient’s subjective experience. The aim of this study was to systematically identify and synthesize the psychometric properties of TMD-specific PROMs. A systematic search was conducted until September 15, 2025. Studies reporting on the development, validation, or cross-cultural adaptation of TMD-specific PROMs were included. Data on psychometric properties were narratively synthesized. The review of 91 studies yielded 43 PROMs. The Fonseca Anamnestic Index was the most extensively validated screening tool. Jaw Functional Limitation Scale, Tampa Scale for Kinesiophobia, and the Oral Health Impact Profile for TMD demonstrated excellent reliability (Cronbach’s α often &gt;0.85-0.95) and cross-cultural validity. Test-retest reliability was strong for most tools (ICCs commonly &gt;0.80), and convergent validity showed moderate-to-strong correlations with established measures. Numerous validated PROMs for TMDs are available, however future work should focus on developing integrated tools with extensive validation.
DOI: 10.22008/fk2/3tsbf0/9zyaqv
title: test_completeness_filter.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/e1cr4l
title: test_get_bufr.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/xktumv
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/oqhmoh
title: DY2.bufr

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/a2exje
title: test_get_bufr_integration.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/bqebzz
title: test_bufr_utilitites.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/2lwzzn
title: test_create_bufr_files.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/dt6r7v
title: WEG_B.bufr

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/yohkgt
title: test_realtime_utilitites.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30468925
title: The global disability-adjusted life years (DALYs) for asthma attributable to nitrogen dioxide pollution from 1990 to 2021 and predicted the trends from 2022 to 2050

description: To support policymakers in developing effective health policies, this study analyzed disability-adjusted life years (DALYs) for asthma attributable to Nitrogen dioxide (NO<sub>2</sub>) pollution from 1990 to 2021 and predicted future trends. Utilizing the Global Burden of Diseases study (GBD) 2021 database, this study analyzed the burden of asthma attributable to NO<sub>2</sub> pollution using DALYs and summary exposure value (SEV), stratified by gender, age, region, country, and socio-demographic index (SDI) level. The asthma risk attributed to NO<sub>2</sub> pollution was estimated using the population attributable fraction (PAF). The Bayesian age-period-cohort model (BAPC) package in R was used to forecast future trends. The global age-standardized DALYs rate (ASDALYs) decreased from 4.80 [95%<i>UI</i>: −4.29 to 17.75] in 1990 to 2.48 [95%<i>UI</i>: −2.26 to 10.30] in 2021. ASDALYs for males were higher than for females. DALYs were the highest proportion in those aged under 15 years. From 1990 to 2021, SEV and ASDALYs increased in Southeast Asia. In 2021, SEV and ASDALYs in Lebanon were 84.35 [95%<i>UI</i>: 0 to 100] and 19.73 [95%<i>UI</i>: −19.51 to 59.84], respectively. The impact of NO<sub>2</sub> on the asthma burden varies across regions with different SDI levels. The global trend in ASDALYs is projected to decline until 2029, reaching approximately 2.96, before increasing to 7.12 by 2050. From 1990 to 2021, the global DALYs for asthma attributed to NO<sub>2</sub> pollution have generally decreased. However, there may be a future rising tendency. Additionally, it is still quite significant for children under the age of 15 and in some underdeveloped regions.
DOI: 10.6084/m9.figshare.30468925.v1
title: The global disability-adjusted life years (DALYs) for asthma attributable to nitrogen dioxide pollution from 1990 to 2021 and predicted the trends from 2022 to 2050

description: To support policymakers in developing effective health policies, this study analyzed disability-adjusted life years (DALYs) for asthma attributable to Nitrogen dioxide (NO<sub>2</sub>) pollution from 1990 to 2021 and predicted future trends. Utilizing the Global Burden of Diseases study (GBD) 2021 database, this study analyzed the burden of asthma attributable to NO<sub>2</sub> pollution using DALYs and summary exposure value (SEV), stratified by gender, age, region, country, and socio-demographic index (SDI) level. The asthma risk attributed to NO<sub>2</sub> pollution was estimated using the population attributable fraction (PAF). The Bayesian age-period-cohort model (BAPC) package in R was used to forecast future trends. The global age-standardized DALYs rate (ASDALYs) decreased from 4.80 [95%<i>UI</i>: −4.29 to 17.75] in 1990 to 2.48 [95%<i>UI</i>: −2.26 to 10.30] in 2021. ASDALYs for males were higher than for females. DALYs were the highest proportion in those aged under 15 years. From 1990 to 2021, SEV and ASDALYs increased in Southeast Asia. In 2021, SEV and ASDALYs in Lebanon were 84.35 [95%<i>UI</i>: 0 to 100] and 19.73 [95%<i>UI</i>: −19.51 to 59.84], respectively. The impact of NO<sub>2</sub> on the asthma burden varies across regions with different SDI levels. The global trend in ASDALYs is projected to decline until 2029, reaching approximately 2.96, before increasing to 7.12 by 2050. From 1990 to 2021, the global DALYs for asthma attributed to NO<sub>2</sub> pollution have generally decreased. However, there may be a future rising tendency. Additionally, it is still quite significant for children under the age of 15 and in some underdeveloped regions.
DOI: 10.22008/fk2/3tsbf0/nbrzzv
title: tx_l3_test1.csv

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.30436787
title: Association between social determinants of health and asthma: a cross-sectional analysis based on NHANES data

description: This study examines the link between social determinants of health (SDoH) and asthma, utilizing data from the National Health and Nutrition Examination Survey (NHANES). We analyzed 39,340 participants, including 5,645 with asthma and 33,695 without. Multivariable logistic regression assessed the SDoH-asthma relationship, with subgroup analyses for effect modifiers. Machine learning models combining demographic, clinical, and SDoH variables were evaluated using area under the curve (AUC). Asthma was more prevalent among younger individuals (mean age: 45.11 years), females (58%), and those with significant differences in BMI, education, marital status, and race (all <i>p</i> &lt; 0.001). Unadjusted models showed a 5.5% increased asthma risk per SDoH index unit (OR = 1.055, <i>p</i> &lt; 0.001), remaining significant after demographic and clinical adjustments (Model 2: OR = 1.045, Model 3: OR = 1.039). Stronger SDoH-asthma associations were found among smokers (OR = 1.08) and diabetics (OR = 1.12), but not in participants with higher education (OR = 1.00). Model 3, including SDoH variables, demonstrated superior predictive performance (training AUC = 0.779) with minimal generalizability loss (ΔAUC = 0.206). SDoH is an independent risk factor for asthma, particularly among smokers, diabetics, and individuals with less education. Incorporating SDoH into predictive models enhances performance and offers insights for clinical and policy interventions.
DOI: 10.6084/m9.figshare.30436787.v1
title: Association Between Social Determinants of Health and Asthma: A Cross-Sectional Analysis Based on NHANES Data

description: This study examines the link between social determinants of health (SDoH) and asthma, utilizing data from the National Health and Nutrition Examination Survey (NHANES). We analyzed 39,340 participants, including 5,645 with asthma and 33,695 without. Multivariable logistic regression assessed the SDoH-asthma relationship, with subgroup analyses for effect modifiers. Machine learning models combining demographic, clinical, and SDoH variables were evaluated using area under the curve (AUC). Asthma was more prevalent among younger individuals (mean age: 45.11 years), females (58%), and those with significant differences in BMI, education, marital status, and race (all P &lt; 0.001). Unadjusted models showed a 5.5% increased asthma risk per SDoH index unit (OR = 1.055, P &lt; 0.001), remaining significant after demographic and clinical adjustments (Model 2: OR = 1.045, Model 3: OR = 1.039). Stronger SDoH-asthma associations were found among smokers (OR = 1.08) and diabetics (OR = 1.12), but not in participants with higher education (OR = 1.00). Model 3, including SDoH variables, demonstrated superior predictive performance (training AUC = 0.779) with minimal generalizability loss (ΔAUC = 0.206). SDoH is an independent risk factor for asthma, particularly among smokers, diabetics, and individuals with less education. Incorporating SDoH into predictive models enhances performance and offers insights for clinical and policy interventions.
DOI: 10.6084/m9.figshare.30436787.v2
title: Association between social determinants of health and asthma: a cross-sectional analysis based on NHANES data

description: This study examines the link between social determinants of health (SDoH) and asthma, utilizing data from the National Health and Nutrition Examination Survey (NHANES). We analyzed 39,340 participants, including 5,645 with asthma and 33,695 without. Multivariable logistic regression assessed the SDoH-asthma relationship, with subgroup analyses for effect modifiers. Machine learning models combining demographic, clinical, and SDoH variables were evaluated using area under the curve (AUC). Asthma was more prevalent among younger individuals (mean age: 45.11 years), females (58%), and those with significant differences in BMI, education, marital status, and race (all <i>p</i> &lt; 0.001). Unadjusted models showed a 5.5% increased asthma risk per SDoH index unit (OR = 1.055, <i>p</i> &lt; 0.001), remaining significant after demographic and clinical adjustments (Model 2: OR = 1.045, Model 3: OR = 1.039). Stronger SDoH-asthma associations were found among smokers (OR = 1.08) and diabetics (OR = 1.12), but not in participants with higher education (OR = 1.00). Model 3, including SDoH variables, demonstrated superior predictive performance (training AUC = 0.779) with minimal generalizability loss (ΔAUC = 0.206). SDoH is an independent risk factor for asthma, particularly among smokers, diabetics, and individuals with less education. Incorporating SDoH into predictive models enhances performance and offers insights for clinical and policy interventions.