cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31065079
title: Supplementary Material for: Additive Association of ABCG2 rs4148155 and SLC22A12 rs75786299 Polymorphisms with Hyperuricemia, Gout and Nephrolithiasis, a Hospital-Based, Case-Control Study

description: Introduction: This study aimed to investigate the interaction between the ABCG2 rs4148155 and SLC22A12 rs75786299 variants and their association with incident gout and nephrolithiasis in the Taiwanese population to better understand the genetic loci regulating hyperuricemia and their contribution to nephrolithiasis development.
Methods: This retrospective case-control study involved 35,280 adults from the Taiwan Precise Medicine Initiative (TPMI) database. We examined the prevalence of gout and ultrasound confirmed nephrolithiasis as the primary and secondary outcome. Logistic regression models were used to explore the associations between genetic variants, serum uric acid levels, incident gout, and nephrolithiasis.
Results: The frequencies of the rs4148155 variant and the rs75786299 variant were 63.2% and 3.7%, respectively. Among participants, 7,056 were gout, and 4,110 had nephrolithiasis. Multivariable odds ratios (ORs) for gout were 1.67 and 1.96 among rs4148155 and rs75786299 carriers, respectively (p=0.01 and p&lt;0.001). For nephrolithiasis, the multivariable ORs were 1.1 and 1.11 for rs4148155 and rs75786299 carriers, respectively (p=0.004 and p=0.32). Sex-stratified analysis revealed an additive risk of gout and nephrolithiasis among carriers of these genetic variants, regardless of gender. Independent risk factors for nephrolithiasis included higher age, male gender, and the presence of gout, hypertension, and hyperlipidemia.
Conclusion: The study highlights a significant association between the rs4148155, rs75786299 variants and the development of gout and nephrolithiasis, indicating an additive risk among carriers. These findings support precision healthcare approaches for individuals with risk genetic variants to target hyperuricemia, gout, and systemic comorbidities, ultimately preventing nephrolithiasis.
DOI: 10.6084/m9.figshare.31065079.v1
title: Supplementary Material for: Additive Association of ABCG2 rs4148155 and SLC22A12 rs75786299 Polymorphisms with Hyperuricemia, Gout and Nephrolithiasis, a Hospital-Based, Case-Control Study

description: Introduction: This study aimed to investigate the interaction between the ABCG2 rs4148155 and SLC22A12 rs75786299 variants and their association with incident gout and nephrolithiasis in the Taiwanese population to better understand the genetic loci regulating hyperuricemia and their contribution to nephrolithiasis development.
Methods: This retrospective case-control study involved 35,280 adults from the Taiwan Precise Medicine Initiative (TPMI) database. We examined the prevalence of gout and ultrasound confirmed nephrolithiasis as the primary and secondary outcome. Logistic regression models were used to explore the associations between genetic variants, serum uric acid levels, incident gout, and nephrolithiasis.
Results: The frequencies of the rs4148155 variant and the rs75786299 variant were 63.2% and 3.7%, respectively. Among participants, 7,056 were gout, and 4,110 had nephrolithiasis. Multivariable odds ratios (ORs) for gout were 1.67 and 1.96 among rs4148155 and rs75786299 carriers, respectively (p=0.01 and p&lt;0.001). For nephrolithiasis, the multivariable ORs were 1.1 and 1.11 for rs4148155 and rs75786299 carriers, respectively (p=0.004 and p=0.32). Sex-stratified analysis revealed an additive risk of gout and nephrolithiasis among carriers of these genetic variants, regardless of gender. Independent risk factors for nephrolithiasis included higher age, male gender, and the presence of gout, hypertension, and hyperlipidemia.
Conclusion: The study highlights a significant association between the rs4148155, rs75786299 variants and the development of gout and nephrolithiasis, indicating an additive risk among carriers. These findings support precision healthcare approaches for individuals with risk genetic variants to target hyperuricemia, gout, and systemic comorbidities, ultimately preventing nephrolithiasis.
DOI: 10.6084/m9.figshare.31065091
title: Supplementary Material for: Characteristics of individuals living with obesity in Germany, weight loss methods attempted, and response over a 12-month period: results from a cross-sectional survey

description: Introduction: This study aimed to describe the demographic and clinical characteristics of individu-als with obesity (IWO) in Germany and to explore weight loss methods and their effectiveness. 
Methods: Data were drawn from a cross-sectional survey of adults with obesity (≥18 years of age) conducted in Germany between November – December 2022 through an online consumer panel. Data were captured using a self-administered questionnaire, and included demographic and clini-cal characteristics, weight loss methods attempted in the last 12 months, and how successful these attempts were. Logistic regression analysis assessed the effect of variables including age, sex and weight loss method on the odds of weight reduction. Data were presented as odds ratios, p-values and confidence intervals. For descriptive statistics, continuous variables were presented as means and standard deviations (SDs). Categorical variables were presented as frequency counts and per-centages. Results: Overall, the 1000 IWO in this study had a mean age (SD) of 42.8 (10.7) years, 94% were White, 45% were female, and mean (SD) body mass index was 37.6 (8.3) kg/m2. In total, 73% had ≥1 comorbid conditions, the most common being musculoskeletal pain (19%). Just 43% were being managed for a weight condition, however, 95% had attempted to lose weight over the previous 12 months, with 33% utilizing an anti-obesity medication and 26% utilizing a calorie-controlled diet. IWO on a calorie-controlled diet or a digital health application were more likely to lose weight than people using other methods (p&lt;0.05). 
Conclusion: Our results highlight gaps in obesity recognition and management in Germany, under-scoring the importance of enhancing access to effective interventions and improving support sys-tems for this population.
DOI: 10.6084/m9.figshare.31065091.v1
title: Supplementary Material for: Characteristics of individuals living with obesity in Germany, weight loss methods attempted, and response over a 12-month period: results from a cross-sectional survey

description: Introduction: This study aimed to describe the demographic and clinical characteristics of individu-als with obesity (IWO) in Germany and to explore weight loss methods and their effectiveness. 
Methods: Data were drawn from a cross-sectional survey of adults with obesity (≥18 years of age) conducted in Germany between November – December 2022 through an online consumer panel. Data were captured using a self-administered questionnaire, and included demographic and clini-cal characteristics, weight loss methods attempted in the last 12 months, and how successful these attempts were. Logistic regression analysis assessed the effect of variables including age, sex and weight loss method on the odds of weight reduction. Data were presented as odds ratios, p-values and confidence intervals. For descriptive statistics, continuous variables were presented as means and standard deviations (SDs). Categorical variables were presented as frequency counts and per-centages. Results: Overall, the 1000 IWO in this study had a mean age (SD) of 42.8 (10.7) years, 94% were White, 45% were female, and mean (SD) body mass index was 37.6 (8.3) kg/m2. In total, 73% had ≥1 comorbid conditions, the most common being musculoskeletal pain (19%). Just 43% were being managed for a weight condition, however, 95% had attempted to lose weight over the previous 12 months, with 33% utilizing an anti-obesity medication and 26% utilizing a calorie-controlled diet. IWO on a calorie-controlled diet or a digital health application were more likely to lose weight than people using other methods (p&lt;0.05). 
Conclusion: Our results highlight gaps in obesity recognition and management in Germany, under-scoring the importance of enhancing access to effective interventions and improving support sys-tems for this population.
DOI: 10.6084/m9.figshare.31065100
title: Supplementary Material for: eGFR Slope Predicts Survival and Renal Outcomes in Myeloperoxidase-Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis: Development and Validation of a Pathogen-Free Model

description: Objectives: This study explores the potential of estimated glomerular filtration rate (eGFR) slope as a noninvasive marker for predicting renal survival and patient survival in myeloperoxidase (MPO) -antineutrophil cytoplasmic antibody (ANCA) -associated glomerulonephritis (AAGN)
Methods: 377 MPO-AAGN patients were included. Kaplan-Meier and Cox regression analyses were performed to assess the predictive value of eGFR slopes for renal survival and patient survival. Then the models were validated by discrimination, calibration, and decision curve analysis. 
Results: The eGFR slope (calculated using a linear mixed-effects model with baseline and follow-up eGFR values up to defined timepoints), higher with better renal recovery, had a median of 2.54 (IQR: 0.49–6.02) ml/min/1.73m²/month for 3 months and 1.90 (IQR: -0.07–4.76) ml/min/1.73m²/month for one year. Multivariate Cox regression identified baseline eGFR (HR=0.96, 95% CI 0.95–0.98, P&lt;0.001) and the 3-month eGFR slope (HR=0.79, 95% CI 0.75–0.85, P&lt;0.001) as independent protective factors for kidney failure. The model incorporating baseline eGFR and the 3-month eGFR slope outperformed existing risk scores for predicting renal survival. The prediction value of the eGFR slope for patient survival was also found. Both a 3-month eGFR slope＞1 ml/min/1.73m²/month (HR=0.47, 95%CI 0.31–0.70, P&lt;0.001) and a 1-year eGFR slope＞1 ml/min/1.73m²/month (HR=0.42, 95%CI 0.28–0.63, P&lt;0.001) were independent protective factors for patient survival. The prediction model with a 1-year eGFR slope showed a prediction ability similar to that of the classical model with kidney failure events.
Conclusion: eGFR slopes are valuable predictors of renal and patients’ survival in MPO-AAGN, providing valuable prognostic information that could enhance risk stratification and clinical decision-making.
DOI: 10.6084/m9.figshare.31065100.v1
title: Supplementary Material for: eGFR Slope Predicts Survival and Renal Outcomes in Myeloperoxidase-Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis: Development and Validation of a Pathogen-Free Model

description: Objectives: This study explores the potential of estimated glomerular filtration rate (eGFR) slope as a noninvasive marker for predicting renal survival and patient survival in myeloperoxidase (MPO) -antineutrophil cytoplasmic antibody (ANCA) -associated glomerulonephritis (AAGN)
Methods: 377 MPO-AAGN patients were included. Kaplan-Meier and Cox regression analyses were performed to assess the predictive value of eGFR slopes for renal survival and patient survival. Then the models were validated by discrimination, calibration, and decision curve analysis. 
Results: The eGFR slope (calculated using a linear mixed-effects model with baseline and follow-up eGFR values up to defined timepoints), higher with better renal recovery, had a median of 2.54 (IQR: 0.49–6.02) ml/min/1.73m²/month for 3 months and 1.90 (IQR: -0.07–4.76) ml/min/1.73m²/month for one year. Multivariate Cox regression identified baseline eGFR (HR=0.96, 95% CI 0.95–0.98, P&lt;0.001) and the 3-month eGFR slope (HR=0.79, 95% CI 0.75–0.85, P&lt;0.001) as independent protective factors for kidney failure. The model incorporating baseline eGFR and the 3-month eGFR slope outperformed existing risk scores for predicting renal survival. The prediction value of the eGFR slope for patient survival was also found. Both a 3-month eGFR slope＞1 ml/min/1.73m²/month (HR=0.47, 95%CI 0.31–0.70, P&lt;0.001) and a 1-year eGFR slope＞1 ml/min/1.73m²/month (HR=0.42, 95%CI 0.28–0.63, P&lt;0.001) were independent protective factors for patient survival. The prediction model with a 1-year eGFR slope showed a prediction ability similar to that of the classical model with kidney failure events.
Conclusion: eGFR slopes are valuable predictors of renal and patients’ survival in MPO-AAGN, providing valuable prognostic information that could enhance risk stratification and clinical decision-making.
DOI: 10.5281/zenodo.18233562
title: Roadmap for electrocaloric films characterization: A numerical study
DOI: 10.15468/dl.vhxnhc
title: Occurrence Download

description: A dataset containing 484 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diarthron linifolium"
 ]
}

 The dataset includes 484 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008637-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18242951
title: Archibaccharis Heering. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18242952
title: Archibaccharis Heering. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.10480916
title: Medical oxygen and respiratory support requirements for patients hospitalised with COVID-19 in 23 low-income and middle-income countries: a prospective, observational cohort study

description: These datasets are derived from the WHO O2CoV2 REDCap project exports (screening, enrolment/follow-up, and facility forms), harmonised across languages, relabelled using the REDCap data dictionary, and further cleaned using documented manual correction files (e.g., day numbering and oxygen flow corrections). The objects below correspond to analysis-ready tables.

The deposit is organised by dataset: for each table we provide (i) a machine-readable CSV file containing the analysis-ready data and (ii) a companion HTML file that documents variable distributions, missingness, and coding, using the original REDCap labels where available.

data_fup_d1 — Daily follow-up, Days 1–7 (D1 form)

Patient-day level dataset containing daily clinical status and respiratory support information for the first week after enrolment. Records were cleaned to remove duplicates and non-standard extra follow-up days, and day numbering was harmonised using manual correction files. Derived variables include harmonised oxygen modality (daily_o2_type), grouped modality (daily_o2_type_2), estimated FiO2 when missing, and computed daily oxygen consumption (daily_o2, daily_o2_lmin), plus saturation-based indicators (e.g., daily_o2sat_90).

data_fup_d2 — Daily follow-up, Days 8–30 (D2 form)

Patient-day level dataset covering days 8–30 of follow-up. Respiratory support modality is reconstructed from the advanced support checklist fields (adv_supptype___*) into harmonised categorical variables (daily_o2_type, daily_o2_type_2). A consistent follow-up day rank (daynum_2) is generated to align with the day numbering used in D1.

data_patient — Patient-level analytical dataset

One-row-per-patient dataset combining enrolment forms (baseline demographics, comorbidities, vaccination status, etc.) with Day 1 follow-up information (data_day1), follow-up length, and derived summaries (e.g., highest oxygen support during hospitalisation). Additional recodes/derived variables include comorbidity counts and categories (n_como, n_como_2), age and BMI categories, harmonised sex/pregnancy fields, and region/facility identifiers.

data_facility — Facility-level analytical dataset

One-row-per-facility dataset describing facility characteristics, oxygen supply systems, electricity sources, bed capacity by care level, staffing, and availability of respiratory support devices. The dataset includes documented manual corrections for beds/devices/staff and derives standardised indicators such as ICU bed counts and percentages, and device/staff rates per 100 beds (e.g., fac_hfnc_100_bed, fac_cpap_100_bed, fac_bme_100_bed).
DOI: 10.5281/zenodo.10480917
title: Medical oxygen and respiratory support requirements for patients hospitalised with COVID-19 in 23 low-income and middle-income countries: a prospective, observational cohort study

description: These datasets are derived from the WHO O2CoV2 REDCap project exports (screening, enrolment/follow-up, and facility forms), harmonised across languages, relabelled using the REDCap data dictionary, and further cleaned using documented manual correction files (e.g., day numbering and oxygen flow corrections). The objects below correspond to analysis-ready tables.

The deposit is organised by dataset: for each table we provide (i) a machine-readable CSV file containing the analysis-ready data and (ii) a companion HTML file that documents variable distributions, missingness, and coding, using the original REDCap labels where available.

data_fup_d1 — Daily follow-up, Days 1–7 (D1 form)

Patient-day level dataset containing daily clinical status and respiratory support information for the first week after enrolment. Records were cleaned to remove duplicates and non-standard extra follow-up days, and day numbering was harmonised using manual correction files. Derived variables include harmonised oxygen modality (daily_o2_type), grouped modality (daily_o2_type_2), estimated FiO2 when missing, and computed daily oxygen consumption (daily_o2, daily_o2_lmin), plus saturation-based indicators (e.g., daily_o2sat_90).

data_fup_d2 — Daily follow-up, Days 8–30 (D2 form)

Patient-day level dataset covering days 8–30 of follow-up. Respiratory support modality is reconstructed from the advanced support checklist fields (adv_supptype___*) into harmonised categorical variables (daily_o2_type, daily_o2_type_2). A consistent follow-up day rank (daynum_2) is generated to align with the day numbering used in D1.

data_patient — Patient-level analytical dataset

One-row-per-patient dataset combining enrolment forms (baseline demographics, comorbidities, vaccination status, etc.) with Day 1 follow-up information (data_day1), follow-up length, and derived summaries (e.g., highest oxygen support during hospitalisation). Additional recodes/derived variables include comorbidity counts and categories (n_como, n_como_2), age and BMI categories, harmonised sex/pregnancy fields, and region/facility identifiers.

data_facility — Facility-level analytical dataset

One-row-per-facility dataset describing facility characteristics, oxygen supply systems, electricity sources, bed capacity by care level, staffing, and availability of respiratory support devices. The dataset includes documented manual corrections for beds/devices/staff and derives standardised indicators such as ICU bed counts and percentages, and device/staff rates per 100 beds (e.g., fac_hfnc_100_bed, fac_cpap_100_bed, fac_bme_100_bed).
DOI: 10.5281/zenodo.18242963
title: Archibaccharis vulneraria (Baker) Heering ex Malag. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18242964
title: Archibaccharis vulneraria (Baker) Heering ex Malag. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.15468/dl.ff5y7x
title: Occurrence Download

description: A dataset containing 88 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is The mammals collection (ZM) of the Muséum national d'Histoire naturelle (MNHN - Paris)",
 "TaxonKey is Axis"
 ]
}

 The dataset includes 88 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008638-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7nah2
title: Occurrence Download

description: A dataset containing 1020876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Morocco",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 1020876 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008600-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fbdy24
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"USA.26.25_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008639-260108223611665/datasets/export for details.
DOI: 10.15468/dl.zrtxus
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.12.2_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008640-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18242972
title: Arctium L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18242971
title: Arctium L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.34740/kaggle/dsv/13718796
title: SkinSAFE Skincare Products (50,000+)
DOI: 10.15468/dl.a4c2j8
title: Occurrence Download

description: A dataset containing 1122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Daphne genkwa"
 ]
}

 The dataset includes 1122 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008641-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.