cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.45a83z
title: Occurrence Download

description: A dataset containing 1080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anaspidesidae",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1080 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064322-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14273820
title: Breast Cancer Gene Expression Dataset
DOI: 10.3886/icpsr39515.v1
title: Measuring and Talking to Patients About the Accuracy of Data Used in Patient-Centered Outcomes Research [Methods Study], North Carolina and Arkansas, 2013-2018
title: Version 1

description: For research studies, researchers can use data about patients' health and treatments from electronic health records, or EHRs. They may also collect self-reported data directly from patients. But a patient's EHR and self-reported data may not always agree. For example, differences may exist between the medicines that patients report taking and the medicines listed in their EHRs. Researchers don't know which of these two data sources is the most accurate.
In this project, the research team looked at EHR and self-reported data to learn which data source was more accurate.
description: The aim of this study included 4 components: (1) to describe the level of agreement between SR medical conditions, procedures, hospitalizations, and smoking status and those
documented in EHRs; (2) to describe the areas of disagreement; (3) to determine which data source is most accurate and under what conditions; and (4) to describe the errors found in each
data source and their root causes.
description: The research team enrolled and obtained EHR records and self-reported data for 5,900 adult patients receiving care at primary care and specialty clinics and hospitals in North Carolina and Arkansas between December 2018 and December 2019. In North Carolina, self-reported data came from the MURDOCK Community registry. In Arkansas, the team collected self-reported data directly from patients.
To measure agreement between the EHR data and self-reported data, the research team linked the data sources and conducted statistical analysis to compare data on 45 items, including 34 medical conditions, 8 procedures, hospitalizations, and smoking status.
To assess the accuracy of each data source, the research team selected and interviewed 610 patients who had discrepancies between their EHR and self-reported data to identify the cause of each discrepancy. Except for age and ethnicity, interviewed patients were representative of the larger sample of study participants. The team created a reference data set for these patients by replacing data with confirmed data from the interviews. The team then compared each item in the EHR and self-reported data with the reference data set to calculate percent agreement, sensitivity, and specificity.
Patients and doctors gave input on the design and conduct of the study.
description: Datasets: 
DS1: Measuring and Talking to Patients About the Accuracy of Data Used in Patient-Centered Outcomes Research [Methods Study], North Carolina and Arkansas, 2013-2018
description: Adult patients who received treatment between December 2018 and December 2019 from clinics and hospitals in North Carolina and Arkansas.
DOI: 10.7910/dvn/fheq5j
title: Replication Data for: Uncertainty in Empirical Economics

description: This is the replication package for "Uncertainty in Empirical Economics," accepted in 2025 by the Journal of Political Economy: Microeconomics.
DOI: 10.3886/icpsr39515
title: Measuring and Talking to Patients About the Accuracy of Data Used in Patient-Centered Outcomes Research [Methods Study], North Carolina and Arkansas, 2013-2018
title: Archival Version

description: For research studies, researchers can use data about patients' health and treatments from electronic health records, or EHRs. They may also collect self-reported data directly from patients. But a patient's EHR and self-reported data may not always agree. For example, differences may exist between the medicines that patients report taking and the medicines listed in their EHRs. Researchers don't know which of these two data sources is the most accurate.
In this project, the research team looked at EHR and self-reported data to learn which data source was more accurate.
description: The aim of this study included 4 components: (1) to describe the level of agreement between SR medical conditions, procedures, hospitalizations, and smoking status and those
documented in EHRs; (2) to describe the areas of disagreement; (3) to determine which data source is most accurate and under what conditions; and (4) to describe the errors found in each
data source and their root causes.
description: The research team enrolled and obtained EHR records and self-reported data for 5,900 adult patients receiving care at primary care and specialty clinics and hospitals in North Carolina and Arkansas between December 2018 and December 2019. In North Carolina, self-reported data came from the MURDOCK Community registry. In Arkansas, the team collected self-reported data directly from patients.
To measure agreement between the EHR data and self-reported data, the research team linked the data sources and conducted statistical analysis to compare data on 45 items, including 34 medical conditions, 8 procedures, hospitalizations, and smoking status.
To assess the accuracy of each data source, the research team selected and interviewed 610 patients who had discrepancies between their EHR and self-reported data to identify the cause of each discrepancy. Except for age and ethnicity, interviewed patients were representative of the larger sample of study participants. The team created a reference data set for these patients by replacing data with confirmed data from the interviews. The team then compared each item in the EHR and self-reported data with the reference data set to calculate percent agreement, sensitivity, and specificity.
Patients and doctors gave input on the design and conduct of the study.
description: Datasets: 
DS1: Measuring and Talking to Patients About the Accuracy of Data Used in Patient-Centered Outcomes Research [Methods Study], North Carolina and Arkansas, 2013-2018
description: Adult patients who received treatment between December 2018 and December 2019 from clinics and hospitals in North Carolina and Arkansas.
DOI: 10.7910/dvn/9dptzr
title: Happy primes generated by the polynomial f(n) = n^2 + n + 17 (sequence)

description: This dataset is a sequence of happy primes generated by the polynomial 
f(n) = n^2 + n + 17. I raise the question of whether there are infinitely many such primes.
DOI: 10.15468/dl.6mfudn
title: Occurrence Download

description: A dataset containing 782 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lycosa singoriensis"
 ]
}

 The dataset includes 782 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064323-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zyhhx
title: Occurrence Download

description: A dataset containing 31999 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Vespa velutina",
 "Year is one of (2024, 2025)",
 "HasCoordinate is true",
 "Geometry POLYGON((3.5 43.5, 12.5 43.5, 12.5 50.5, 3.5 50.5, 3.5 43.5))"
 ]
}

 The dataset includes 31999 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064324-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c6tzhq
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Psammaspididae",
 "HasCoordinate is true"
 ]
}

 The dataset includes 36 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064325-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/gjda1i
title: Neighborhood-Scale Mixed-Methods Dataset for Household Energy Renovation Analysis

description: This paper presents a neighborhood-scale mixed-methods dataset for the analysis of household energy renovation. The dataset combines three complementary data sources collected in an urban residential neighborhood in Liège, Belgium: (i) a door-to-door household survey documenting socio-demographic characteristics, dwelling attributes, energy use, and renovation status; (ii) qualitative focus group data capturing expert and stakeholder perspectives on energy renovation and energy poverty; and (iii) façade infrared thermographic images acquired from the public space to document relative thermal performance and heat loss patterns of residential buildings. The dataset enables the joint analysis of building conditions, household characteristics, and renovation contexts at the neighborhood scale. It is intended to support research on energy renovation, energy poverty, and the socio-technical assessment of residential buildings, as well as methodological studies that combine social data and infrared thermography.
DOI: 10.15468/dl.xk9fw9
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa mulleri"
 ]
}

 The dataset includes 89 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064326-251120083545085/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.25316500
title: coco formatted infant pose estimates for: Computer vision to automatically assess infant neuromotor risk

description: COCO formatted 2D skeletal keypoints for YouTube and Clinical datasets from: Computer vision to automatically assess infant neuromotor risk (Chambers et al 2020) extracted with ViTPose-H implemented in MMPose. For original dataset and data see: https://figshare.com/s/10034c230ad9b2b2a6a4Includes:json files with bounding boxes and 2D keypoints/confidencesvideo metadata (fps, original dimensions)Data descriptions:Youtube Dataset: 94 infants, 19 excluded19 annotations removed for meeting one or more of the following exclusion criteria:8 Partially overlapping twins4 NICU and/or hospital settings1 In water1 On a rocker2 Face occluded by caregiver and/or toy3 Low contrast/very poor video qualityClinical Dataset: 19 infants (31 videos total)Additional data available in original figshare
DOI: 10.6084/m9.figshare.25316500.v1
title: coco formatted infant pose estimates for: Computer vision to automatically assess infant neuromotor risk

description: COCO formatted 2D skeletal keypoints for YouTube and Clinical datasets from: Computer vision to automatically assess infant neuromotor risk (Chambers et al 2020) extracted with ViTPose-H implemented in MMPose. For original dataset and data see: https://figshare.com/s/10034c230ad9b2b2a6a4Includes:json files with bounding boxes and 2D keypoints/confidencesvideo metadata (fps, original dimensions)Data descriptions:Youtube Dataset: 94 infants, 19 excluded19 annotations removed for meeting one or more of the following exclusion criteria:8 Partially overlapping twins4 NICU and/or hospital settings1 In water1 On a rocker2 Face occluded by caregiver and/or toy3 Low contrast/very poor video qualityClinical Dataset: 19 infants (31 videos total)Additional data available in original figshare
DOI: 10.6084/m9.figshare.25316500.v2
title: coco formatted infant pose estimates for: Computer vision to automatically assess infant neuromotor risk

description: COCO formatted 2D skeletal keypoints for YouTube and Clinical datasets from: Computer vision to automatically assess infant neuromotor risk (Chambers et al 2020) extracted with ViTPose-H implemented in MMPose. For original dataset and data see: https://figshare.com/s/10034c230ad9b2b2a6a4Includes:json files with bounding boxes and 2D keypoints/confidencesvideo metadata (fps, original dimensions)Data descriptions:Youtube Dataset: 94 infants, 19 excluded19 annotations removed for meeting one or more of the following exclusion criteria:8 Partially overlapping twins4 NICU and/or hospital settings1 In water1 On a rocker2 Face occluded by caregiver and/or toy3 Low contrast/very poor video qualityClinical Dataset: 19 infants (31 videos total)Additional data available in original figshare
DOI: 10.15468/dl.7n96ke
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parartemiidae",
 "HasCoordinate is true"
 ]
}

 The dataset includes 134 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064328-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.prj4hx
title: Occurrence Download

description: A dataset containing 28688 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is one of (Canada, United States of America)",
 "TaxonKey is Betula papyrifera"
 ]
}

 The dataset includes 28688 records from 226 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064327-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr39516.v1
title: Methods for Prioritizing Surrogate Desired Health Outcomes for Patients [Methods Study], Washington, DC and Maryland, 2013-2018
title: Version 1

description: Children with intellectual and cognitive disabilities often have problems with reasoning, learning, and problem solving. With such challenges, caregivers may have to make choices for their children about their health care and treatments. These children may need such help into young adulthood.
In this study, the research team wanted to find out what matters most to caregivers about their children's care. Caregivers had children with an intellectual or cognitive disability plus a mental health condition, such as depression or anxiety. The team interviewed caregivers about their preferences. Then the team used this information to design a survey. A larger group of caregivers filled out the survey.
description: The objectives were to (1) elicit surrogate-defined meaningful outcomes for their child with developmental impairment and a coexisting psychiatric disorder, (2) identify the priorities for outcomes for English- and Spanish-speaking surrogates, and (3) apply valid and reliable approaches for generating meaningful evidence to inform surrogate decision-making.
description: The research team used a mixed-methods approach to identify patient-centered outcomes of greatest importance to caregivers when making decisions about medication treatment and care management for their children with intellectual and cognitive disabilities and a mental illness.
In the first part of the study, the research team held interviews and focus groups with 58 caregivers of children age 26 or younger to identify meaningful treatment outcomes.
The research team used the results of the interviews and focus groups to develop a survey. The team designed the survey as a discrete choice experiment and a conjoint analysis. Caregivers selected a preferred care management profile from three shown. The 18 questions displayed different profile options by varying the combination of management options. The team used a best-worst scaling experiment to elicit from caregivers the most important outcomes from their child's medication treatment.
The research team gave the online survey to 413 caregivers. Of these, 71% were a child's mother, 22% were a child's father, and 6% were another relation. In addition, 56% were white, 24% were Hispanic, 13% were black, 4% were Asian, and 3% were another race. Also, 32% made more than $75,000 per year, 75% had at least a college degree, and 80% were married.
Eight staff members from advocacy groups and support groups helped the research team with outreach and participant recruitment. Four caregivers of children with intellectual or cognitive disabilities and a mental health condition helped with research activities, such as data collection. Two state administrators provided advice during the study.
description: Datasets: 
DS1: Methods for Prioritizing Surrogate Desired Health Outcomes for Patients [Methods Study], Washington, DC and Maryland, 2013-2018
description: Hispanic participants from communities in Maryland and Washington DC.
DOI: 10.3886/icpsr39516
title: Methods for Prioritizing Surrogate Desired Health Outcomes for Patients [Methods Study], Washington, DC and Maryland, 2013-2018
title: Archival Version

description: Children with intellectual and cognitive disabilities often have problems with reasoning, learning, and problem solving. With such challenges, caregivers may have to make choices for their children about their health care and treatments. These children may need such help into young adulthood.
In this study, the research team wanted to find out what matters most to caregivers about their children's care. Caregivers had children with an intellectual or cognitive disability plus a mental health condition, such as depression or anxiety. The team interviewed caregivers about their preferences. Then the team used this information to design a survey. A larger group of caregivers filled out the survey.
description: The objectives were to (1) elicit surrogate-defined meaningful outcomes for their child with developmental impairment and a coexisting psychiatric disorder, (2) identify the priorities for outcomes for English- and Spanish-speaking surrogates, and (3) apply valid and reliable approaches for generating meaningful evidence to inform surrogate decision-making.
description: The research team used a mixed-methods approach to identify patient-centered outcomes of greatest importance to caregivers when making decisions about medication treatment and care management for their children with intellectual and cognitive disabilities and a mental illness.
In the first part of the study, the research team held interviews and focus groups with 58 caregivers of children age 26 or younger to identify meaningful treatment outcomes.
The research team used the results of the interviews and focus groups to develop a survey. The team designed the survey as a discrete choice experiment and a conjoint analysis. Caregivers selected a preferred care management profile from three shown. The 18 questions displayed different profile options by varying the combination of management options. The team used a best-worst scaling experiment to elicit from caregivers the most important outcomes from their child's medication treatment.
The research team gave the online survey to 413 caregivers. Of these, 71% were a child's mother, 22% were a child's father, and 6% were another relation. In addition, 56% were white, 24% were Hispanic, 13% were black, 4% were Asian, and 3% were another race. Also, 32% made more than $75,000 per year, 75% had at least a college degree, and 80% were married.
Eight staff members from advocacy groups and support groups helped the research team with outreach and participant recruitment. Four caregivers of children with intellectual or cognitive disabilities and a mental health condition helped with research activities, such as data collection. Two state administrators provided advice during the study.
description: Datasets: 
DS1: Methods for Prioritizing Surrogate Desired Health Outcomes for Patients [Methods Study], Washington, DC and Maryland, 2013-2018
description: Hispanic participants from communities in Maryland and Washington DC.
DOI: 10.15468/dl.k5q4g6
title: Occurrence Download

description: A dataset containing 243 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa nicefori"
 ]
}

 The dataset includes 243 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064329-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14273865
title: Dataset jadwal event lariku.info
DOI: 10.15468/dl.wbm4n7
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bolitoglossa nigrescens"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064331-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8eqjwy
title: Occurrence Download

description: A dataset containing 1144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Thamnocephalidae",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1144 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064332-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17428031
title: Design and Evaluation of a Modular Multilingual RAG Architecture for Trustworthy and Auditable AI Systems: The PrivacyEthical Chat Tool

description: Context and motivation.  The growing adoption of Large Language Models (LLMs) in institutional settings introduces new challenges related to trustworthiness, transparency, and ethical compliance. In critical domains such as ethics, privacy, and information security, it is essential that automated systems provide traceable and contextually accurate answers. Retrieval-Augmented Generation (RAG) offers a promising approach to meet these demands by grounding generated content in external, verifiable sources. However, little research has examined how RAG architectures can be effectively designed and evaluated for multilingual and governance-sensitive environments. Question/problem. This study investigates how a modular RAG architecture can be designed to produce high-precision and auditable outputs in multilingual institutional contexts. Specifically, it explores how heuristic language detection, domain-specific routing, and semantic retrieval can be combined to ensure accuracy, interpretability, and scalability in automated support systems.  Principal ideas/results. We designed and implemented a three-stage RAG pipeline comprising (i) heuristic language detection, (ii) domain-specific classification, and (iii) semantic retrieval using high-precision vector embeddings. The system was evaluated on a bilingual (Portuguese/English) dataset covering ethics and  privacy requirements. Experimental results indicate an accuracy of 84.3% in language detection, embeddings achieved clear semantic separation between domains, and cosine similarity scores for relevant documents consistently exceeded 0.7, demonstrating robustness and retrieval effectiveness. Additionally, the architecture successfully generated structured and traceable user stories grounded in validated ethics and privacy requirements.  Contribution. This work contributes a validated multilingual RAG architecture that operationalizes principles of trustworthy and explainable AI through transparent retrieval and accountable response generation. The proposed design supports institutional governance and accountability by combining accuracy, interpretability, and scalability, advancing the development of ethical, auditable, and reliable AI systems across organizational contexts.