cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2023-07.2849934/2404740
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850520/2404741
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850037/2404742
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850017/2404743
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849906/2404744
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850034/2404745
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850141/2404746
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850466/2404747
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849875/2404748
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850065/2404749
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850067/2404750
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849902/2404751
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850533/2404752
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851025/2404753
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850523/2404754
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850525/2404755
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849978/2404756
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850018/2404757
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849914/2404758
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849900/2404759
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850132/2404760
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849891/2404761
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849898/2404762
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850038/2404763
title: Data for EMSL Project 51287 from July 2023
DOI: 10.15468/dl.2swx48
title: Occurrence Download

description: A dataset containing 56146 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is The Second Breeding Bird Atlas of Vermont (2003-2007)"
 ]
}

 The dataset includes 56146 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031337-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-07.2850072/2404764
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849874/2404765
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850546/2404766
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850824/2404767
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851105/2404768
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849909/2404769
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849917/2404770
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851097/2404771
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850915/2404772
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849873/2404773
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851200/2404774
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850000/2404775
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849992/2404776
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849991/2404777
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849879/2404778
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850056/2404779
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851204/2404780
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849884/2404781
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849936/2404782
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850515/2404783
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849967/2404784
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850035/2404785
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850518/2404786
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849970/2404787
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849977/2404788
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2849979/2404789
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850153/2404790
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2851196/2404791
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850405/2404792
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2850461/2404793
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850392/2404794
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850389/2404795
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850411/2404796
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850426/2404797
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850551/2404798
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850439/2404799
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850435/2404800
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850450/2404801
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850409/2404802
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850463/2404803
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850406/2404804
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850428/2404805
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850391/2404806
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850397/2404807
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850451/2404808
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850396/2404809
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850427/2404810
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850410/2404811
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850550/2404812
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850432/2404813
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850395/2404814
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850369/2404815
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850340/2404816
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850457/2404817
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850462/2404818
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850433/2404819
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850431/2404820
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850403/2404821
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850388/2404822
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850393/2404823
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850407/2404824
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850424/2404825
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850404/2404826
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850425/2404827
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850449/2404828
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850458/2404829
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850408/2404830
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850342/2404831
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850460/2404832
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850429/2404833
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850398/2404834
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850434/2404835
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850441/2404836
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850399/2404837
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850400/2404838
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850452/2404839
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850367/2404840
title: Data for EMSL Project 47418 from July 2023
DOI: 10.25582/data.2023-07.2850394/2404841
title: Data for EMSL Project 47418 from July 2023
DOI: 10.5281/zenodo.12789271
title: Code and data for "Comparing cost-effectiveness of radio and drone telemetry with playback surveys for assessing translocation outcomes"

description: Overview

Code and data used for the analyses of Stone et al., 2024 "Comparing cost-effectiveness of radio and drone telemetry with playback surveys for assessing translocation outcomes". Forthcoming in Journal of Applied Ecology

Abstract

1. Post-release monitoring is critical for assessing translocation outcomes. Yet the quality of information gained from monitoring can vary greatly, and perceived monitoring costs often results in reduced monitoring effort.  Selecting cost-effective monitoring strategies that provide high quality data is therefore important for assessing translocation outcomes and making informed management decisions. 2. To compare how monitoring strategy affects information gained, we monitored a toutouwai/North Island robin (Petroica longipes) reintroduction in Aotearoa New Zealand, based on monitoring objectives of determining survival, site fidelity and whether the extent of management was large enough to protect dispersing individuals. We compared how these objectives were met through four monitoring strategies: 1) comprehensive surveys with ground radio telemetry and playback; 2) aerial drone telemetry; 3) dedicated playback by trained surveyors and 4) opportunistic playback by predator control contractors. We undertook a viewshed analysis to determine search coverage of each strategy and compared detection rates, efficiency, and cost. 3. Comprehensive ground telemetry and playback, while costly, covered the largest area and provided the most accurate data on dispersal, survival and the translocation outcome.  In comparison, opportunistic playback monitoring detected substantially fewer individuals, giving a false impression of low site fidelity and survival and a failed translocation. Although drone telemetry had considerable site-specific limitations, which limited its effectiveness during our study, it was the most cost-effective with a high detection rate and low search effort. 4. Synthesis and applications: Our study shows the value of intensive monitoring in facilitating management decisions for wildlife translocations. Comprehensive telemetry and playback, while costly, were invaluable for gaining high quality information on the translocation outcome. Without suitable monitoring, reintroduction outcomes can be difficult to assess and potentially result in unnecessary, ineffective, or overly expensive management actions. We recommend that monitoring intensity and methodology should reflect the site, species and level of uncertainty regarding the translocation outcome. Prioritising monitoring can help reduce long-term costs, increase quality of information gained and allow for more informed management decisions that can improve subsequent translocation outcomes.
DOI: 10.15468/dl.e6sghz
title: Occurrence Download

description: A dataset containing 27951 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sphyrna lewini (Griffith & Smith, 1834)"
 ]
}

 The dataset includes 27951 records from 256 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031339-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/ds/5322044
title: EAVDD Violence Dataset
DOI: 10.15468/dl.utp2r8
title: Occurrence Download

description: A dataset containing 80783 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is North East Scotland Breeding Bird Atlas 2002-06"
 ]
}

 The dataset includes 80783 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031340-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.lhsdw1zf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (489 records)
DOI: 10.5281/zenodo.12789304
title: Aqueous phase processing affects the formation and size distribution of aerosol organic functional groups during heavy pollution

description: Data from Hefei,a major urban city in Eastern China, including chemical composition, Infrared spectrum (IR), and pollution air data.
DOI: 10.15468/dl.vchz6q
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Nepal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Roscoea alpina Royle"
 ]
}

 The dataset includes 21 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031341-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12789292
title: Predicting Bug-Inducing Commits Using Software Quality Metrics
DOI: 10.5281/zenodo.12789293
title: Predicting Bug-Inducing Commits Using Software Quality Metrics
DOI: 10.15468/dl.gesshe
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.19_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Hierochloe odorata (L.) P.Beauv."
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031342-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxwset
title: Occurrence Download

description: A dataset containing 77658 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Sylvilagus obscurus Chapman, Cramer, Deppenaar & Robinson, 1992, Sylvilagus aquaticus (Bachman, 1837), Sylvilagus floridanus (J.A.Allen, 1890), Lepus europaeus Pallas, 1778, Lepus americanus Erxleben, 1777)"
 ]
}

 The dataset includes 77658 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031343-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26113675
title: Early Maladaptive Schemas Shape Personality and Affect in Non-Clinical Adults

description: Background: Early maladaptive schemas (EMS) play a significant role in psychological functioning, but their prevalence and impact in non-clinical populations remain understudied. This research investigates the distinct clusters of EMS in a non-clinical adult sample and examines their relationships with personality traits and affective experiences.Methods: Ninety-one adults (mean age 42.5 years) completed the Young Schema Questionnaire (YSQ-S3), Big Five Inventory (BFI), Positive and Negative Affect Schedule (PANAS), and Dispositional Positive Emotion Scales (DPES). K-means clustering was employed to identify EMS patterns, followed by Kruskal-Wallis tests, independent-samples t-tests, and Spearman's rank correlations to analyze relationships between EMS clusters, personality traits, and affective states.Results: Three distinct clusters of EMS intensity emerged: Low, Medium, and High EMS. Neuroticism showed significant differences across all clusters (p &lt; .001) and correlated positively with most EMS. Entitlement/Grandiosity uniquely showed no correlation with Neuroticism but a strong negative correlation with Agreeableness. Within the Medium cluster, which scored highest on Entitlement/Grandiosity, a follow-up supervised k-means analysis revealed two unique personality types distinguished by Neuroticism and Extraversion. The High EMS cluster exhibited significantly greater Negative Affect than the Low cluster (p = .002). Dispositions towards Love and Pride showed significant differences across clusters (p ≤ .001) and negative correlations with specific EMS.Conclusions: This study reveals distinct patterns of EMS in non-clinical adults, highlighting Neuroticism as a key factor in EMS intensity and Entitlement/Grandiosity as a unique schema. The findings suggest potential targets for interventions, such as addressing Neuroticism and fostering positive emotions, especially love, to mitigate maladaptive schemas in non-clinical populations.
DOI: 10.6084/m9.figshare.26113675.v1
title: Early Maladaptive Schemas Shape Personality and Affect in Non-Clinical Adults

description: Background: Early maladaptive schemas (EMS) play a significant role in psychological functioning, but their prevalence and impact in non-clinical populations remain understudied. This research investigates the distinct clusters of EMS in a non-clinical adult sample and examines their relationships with personality traits and affective experiences.Methods: Ninety-one adults (mean age 42.5 years) completed the Young Schema Questionnaire (YSQ-S3), Big Five Inventory (BFI), Positive and Negative Affect Schedule (PANAS), and Dispositional Positive Emotion Scales (DPES). K-means clustering was employed to identify EMS patterns, followed by Kruskal-Wallis tests, independent-samples t-tests, and Spearman's rank correlations to analyze relationships between EMS clusters, personality traits, and affective states.Results: Three distinct clusters of EMS intensity emerged: Low, Medium, and High EMS. Neuroticism showed significant differences across all clusters (p &lt; .001) and correlated positively with most EMS. Entitlement/Grandiosity uniquely showed no correlation with Neuroticism but a strong negative correlation with Agreeableness. Within the Medium cluster, which scored highest on Entitlement/Grandiosity, a follow-up supervised k-means analysis revealed two unique personality types distinguished by Neuroticism and Extraversion. The High EMS cluster exhibited significantly greater Negative Affect than the Low cluster (p = .002). Dispositions towards Love and Pride showed significant differences across clusters (p ≤ .001) and negative correlations with specific EMS.Conclusions: This study reveals distinct patterns of EMS in non-clinical adults, highlighting Neuroticism as a key factor in EMS intensity and Entitlement/Grandiosity as a unique schema. The findings suggest potential targets for interventions, such as addressing Neuroticism and fostering positive emotions, especially love, to mitigate maladaptive schemas in non-clinical populations.
DOI: 10.15468/dl.3vpcng
title: Occurrence Download

description: A dataset containing 292 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Molendoa sendtneriana (Bruch & Schimp.) Limpr."
 ]
}

 The dataset includes 292 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031345-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3mzmep
title: Occurrence Download

description: A dataset containing 4516 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Castanea mollissima Blume"
 ]
}

 The dataset includes 4516 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031346-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wuyj9u
title: Occurrence Download

description: A dataset containing 28385 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Plan de vertebrados de la Patagonia"
 ]
}

 The dataset includes 28385 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031347-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2qrqck
title: Occurrence Download

description: A dataset containing 1032426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2024-05-25",
 "EventDate is less than or equal to 2024-06-24",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 1032426 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031344-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfayyp
title: Occurrence Download

description: A dataset containing 621 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Monoclea gottschei Lindb."
 ]
}

 The dataset includes 621 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031349-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26341891
title: selfmed

description: self_medication<br>
DOI: 10.6084/m9.figshare.26341891.v1
title: selfmed

description: self_medication<br>
DOI: 10.6084/m9.figshare.26326936
title: Belete GK et al_PLos ONE_Diabetes_data.savPrevalence of Diabetes Mellitus in Hawassa Town, Ethiopia

description: This dataset was collected in 2023 for a study on the prevalence of diabetes mellitus in Hawassa Town, Ethiopia.