cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10679815
title: Test
DOI: 10.5281/zenodo.10679816
title: Test
DOI: 10.17632/4kc5dn9ftm.1
title: RNA-sequencing and metabolomics data of "A clinical-stage direct Nrf2 activator suppresses osteoclast differentiation via iron-ornithine axis"

description: This dataset includes the raw RNA-sequencing (counts) and metabolomics data generated in the study. All the RNA-sequencing and metabolomic assays were performed on bone marrow derived macrophages of wildtype or Nrf2-KO C57BL6J mice. For RNA sequencing, BMDMs were seeded on 6-well plates with 1×10^6 cells per well. Each group had five replicates from different mice and cells were collected after three days of culture and intervention. Total RNA was extracted with TRIzol (Takara, Japan). For the metabolomic assay, BMDMs were cultured in 10-cm dishes with 1×10^7 cells per dish. After intervention, cells were collected in prechilled 80% methanol and were subjected to three freeze-thaw cycles in liquid nitrogen. LC-MS/MS was used for the untargeted metabolomic assay. All assays were performed by Novogene (Beijing, China).
DOI: 10.17632/dwfgxrh7tn.7
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access 

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2021), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). 

AHRF data are updated annually, and are also provided in R (*.RDS) and Stata-friendly (*.dta) formats.

Please cite the following publication when using this dataset:

KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779 

These data were originally collected for the following research article:

Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501

Special thanks to everyone who sent us historical AHRFs (Randy Ellis, Kimberley Geissler, Wenjia Zhu, and others) and to Monica Aswani for bringing a data error to our attention.
DOI: 10.17632/dwfgxrh7tn.8
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access 

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000-2001, 2004-2022), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). 

AHRF data are updated annually, and are also provided in R (*.RDS) and Stata-friendly (*.dta) formats.

Please cite the following publication when using this dataset:

KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779 

These data were originally collected for the following research article:

Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501

Special thanks to everyone who sent us historical AHRFs (Randy Ellis, Kimberley Geissler, Wenjia Zhu, and others) and to Monica Aswani for bringing a data error to our attention.
DOI: 10.17632/dwfgxrh7tn.6
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). Please cite the following publication when using this dataset: KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779 These data were originally collected for the following research article: Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501 Special thanks to everyone who sent us historical AHRFs (Randy Ellis, Kimberley Geissler, Wenjia Zhu, and others) and to Monica Aswani for bringing a data error to our attention.
DOI: 10.17632/dwfgxrh7tn.3
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). Please cite the following publication when using this dataset: KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (in press). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access, Data in Brief. These data were originally collected for the following research article: Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501
DOI: 10.17632/dwfgxrh7tn.4
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). Please cite the following publication when using this dataset: KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779 These data were originally collected for the following research article: Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501
DOI: 10.17632/dwfgxrh7tn.5
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). Please cite the following publication when using this dataset: KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (2021). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access. Data in Brief 35: e106779. https://doi.org/10.1016/j.dib.2021.106779 These data were originally collected for the following research article: Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (2021). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence 219: e108501. https://doi.org/10.1016/j.drugalcdep.2020.108501 Special thanks to everyone who sent us historical AHRFs (Randy Ellis, Kimberley Geissler, Wenjia Zhu, and others) and to Monica Aswani for bringing a data error to our attention.
DOI: 10.17632/jwwd7jd7wc.2
title: Dataset of analysis of the large language model-powered chatbots’ advice on help to a non-breathing victim

description: This dataset contains results of evaluation of performance of the new Bing (Microsoft Corporation, Redmond, Washington, USA) and Bard (Google LLC, Mountain View, California, USA) large language model-powered chatbots in terms of quality of the chatbot-generated advice on how to give help to a non-breathing victim.
In May 2023, the chatbots were repeatedly inquired (20 times per the chatbot) “What to do if someone is not breathing?” (search language: English, search region: the United Kingdom). Additionally, the chatbots were requested to rate their original responses for compliance with the Resuscitation Council UK guidelines using a 10-point scale (from 1 “very low compliance” to 10 “very high compliance”); to report whether the responses contain any guidelines-inconsistent advice; and to correct the responses to ensure full compliance with the guidelines.
Original and corrected chatbots’ responses containing instructions on help to a non-breathing victim were manually assessed for compliance with the 2021 Resuscitation Council UK Guidelines on adult Basic Life Support [Perkins et al., 2021] using a predeveloped checklist. Adherence to the guidelines was rated for each item of the checklist as True (where checklist item wording was satisfied in full), Partially True (where checklist item wording was satisfied in part) or Not True (when the corresponding instruction was omitted from the chatbot response). 
Additionally, the chatbots’ responses were evaluated for number of sentences and for readability based on the Flesch-Kincaid Grade Level [Kincaid et al., 1975].
Content of the webpages referenced by the chatbots as a source for generating the responses was evaluated for quality using the same checklist.
DOI: 10.17632/pg3s4hw68k.1
title: Research_data_frontier

description: This dataset comprises a comprehensive collection of reviews pertaining to clothing products and serves as a valuable resource for multilabel classification research. Each data entry is meticulously annotated with relevant labels, allowing researchers to explore various dimensions of the clothing products being reviewed. The dataset offers a rich diversity of perspectives and opinions, enabling the development and evaluation of robust classification models that can accurately predict multiple aspects of a given clothing item. With its focus on multilabel classification, this data contributes significantly to advancing the understanding and application of machine learning algorithms in the fashion industry.
DOI: 10.17632/pg3s4hw68k.2
title: Consumer Review of Clothing Product

description: The dataset is collected on our own from various sources. This dataset comprises a comprehensive collection of reviews pertaining to clothing products and serves as a valuable resource for multilabel classification research. Each data entry is meticulously annotated with relevant labels, allowing researchers to explore various dimensions of the clothing products being reviewed. The dataset offers a rich diversity of perspectives and opinions, enabling the development and evaluation of robust classification models that can accurately predict multiple aspects of a given clothing item. With its focus on multilabel classification, this data contributes significantly to advancing the understanding and application of machine learning algorithms in the fashion industry.
DOI: 10.17632/dwfgxrh7tn.1
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: Please cite the following publication when using this dataset: K.N. Griffith, Y. Feyman, S.G. Auty, E.L. Crable, T.W. Levengood. (in press). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access, Data in Brief.
DOI: 10.17632/dwfgxrh7tn.2
title: County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access

description: This repository includes data from the Health Resources & Services Administration's Area Health Resources Files (years 2000, 2004-2019), CDC Wonder, National Conference of State Legislatures, and the Drug Enforcement Agency's Automation of Reports and Consolidated Orders System (ARCOS). Please cite the following publication when using this dataset: KN Griffith, Y Feyman, SG Auty, EL Crable, TW Levengood. (in press). County-level data on U.S. opioid distributions, demographics, healthcare supply, and healthcare access, Data in Brief. These data were originally collected for the following research article: Griffith, KN, Feyman, Y, Crable, EL, & Levengood, TW. (in press). “Implications of county-level variation in U.S. opioid distribution.” Drug and Alcohol Dependence.
DOI: 10.17632/jwwd7jd7wc.1
title: Dataset of analysis of the large language model-powered chatbots’ advice on help to a non-breathing victim

description: This dataset contains results of evaluation of performance of the new Bing (Microsoft Corporation, Redmond, Washington, USA) and Bard (Google LLC, Mountain View, California, USA) large language model-powered chatbots in terms of quality of the chatbot-generated advice on how to give help to a non-breathing victim.
In May 2023, the chatbots were repeatedly inquired (20 times per the chatbot) “What to do if someone is not breathing?” (search language: English, search region: the United Kingdom). Additionally, the chatbots were requested to rate their original responses for compliance with the Resuscitation Council UK guidelines using a 10-point scale (from 1 “very low compliance” to 10 “very high compliance”); to report whether the responses contain any guidelines-inconsistent advice; and to correct the responses to ensure full compliance with the guidelines.
Original and corrected chatbots’ responses containing instructions on help to a non-breathing victim were manually assessed for compliance with the 2021 Resuscitation Council UK Guidelines on adult Basic Life Support [Perkins et al., 2021] using a predeveloped checklist. Adherence to the guidelines was rated for each item of the checklist as True (where checklist item wording was satisfied in full), Partially True (where checklist item wording was satisfied in part) or Not True (when the corresponding instruction was omitted from the chatbot response). 
Additionally, the chatbots’ responses were evaluated for number of sentences and for readability based on the Flesch-Kincaid Grade Level [Kincaid et al., 1975].
Content of the webpages referenced by the chatbots as a source for generating the responses was evaluated for quality using the same checklist.
DOI: 10.17632/kjzvzt8b7h.2
title: Dataset of the Delphi study on development of an expert consensus-based checklist for quality appraisal of educational resources on adult Basic Life Support

description: This dataset presents original data and supplementary materials for the international two-round Delphi study that was carried out in November-December 2022 to develop an expert consensus-based checklist for quality appraisal of educational resources on adult Basic Life Support.
The dataset contains:
1. Blank questionnaire forms used for the first round and the second round of the Delphi survey (Dataset_Delphi 1st round Survey Form.xlsx and Dataset_Delphi 2nd round Survey Form.xlsx, respectively)
2. Tabulated anonymised expert ratings and comments, calculated quantitative expert group results (each item median, lowest and highest ratings, percentage of experts who rated an item ≥7 points, percentage of experts who rated an item ≤3 points) for the first round and the second round of the Delphi survey (Dataset_Delphi 1st round Results.xlsx and Dataset_Delphi 2nd round Results.xlsx, respectively)
3. A summary of results for the first round, that was sent to the participants before the second round (Dataset_Delphi 1st round Summary & 2nd round Instructions.pdf), and marked modifications made to the checklist based on the first round results (Dataset_Delphi 2nd round Survey Form.xlsx)
4. The blank template of the produced checklist in its final layout (Dataset_Final Checklist Template.xlsx).
DOI: 10.17632/4z4pk9rxb3.1
title: IRAK1/IRF5 axis initiates IL-12 response by dendritic cells and control of Toxoplasma gondii infection

description: Data for the paper "The IRAK1/IRF5 axis initiates IL-12 response by dendritic cells and control of Toxoplasma gondii infection" (DOI: 10.1016/j.celrep.2024.113795)
DOI: 10.17632/rgcf2dyhrn.1
title: data

description: data for bibliometric analysis
DOI: 10.17632/5wk86jvkdj.2
title: Dataset of analysis of the new Bing chatbot responses to queries concerning first aid in choking

description: The dataset contains results of evaluation of the new Bing (Microsoft Corporation, Redmond, Washington, USA) chatbot responses to search queries concerning provision of help in choking.
On 4th April 2023, the chatbot was repeatedly (n=20) queried “What to do if someone is choking?” (search language: English, search region: the United Kingdom). The chatbot responses were collected and independently evaluated by the authors for conformity with the guidelines on first aid in foreign body airway obstruction using an eight-item checklist predeveloped by the authors based on the International Federation of Red Cross and Red Crescent Societies’ International First Aid, Resuscitation, and Education Guidelines 2020 [IFRC, 2020], and the European Resuscitation Council Guidelines for Resuscitation 2015 [Perkins et al., 2015] and 2021 [Olasveengen et al., 2021]. Adherence to the guidelines was rated for each item of the checklist as True (where checklist item wording was satisfied in full), Partially True (where checklist item wording was satisfied in part) or Not True (when the corresponding first aid instruction was omitted from the chatbot response). Additionally, the chatbot responses were evaluated for readability based on the Flesch-Kincaid Grade Level.
Webpages referenced by the chatbot as a source for generating its responses were classified as Academic, Charity/Non-profit entity, Commercial, and Government, and content of the source webpages was evaluated for quality of the first aid instructions using the same checklist with a focus on source article text.
DOI: 10.17632/5wk86jvkdj.1
title: Dataset of analysis of the new Bing chatbot responses to queries concerning first aid in choking

description: The dataset contains results of evaluation of the new Bing (Microsoft Corporation, Redmond, Washington, USA) chatbot responses to search queries concerning provision of help in choking.
On 4th April 2023, the chatbot was repeatedly (n=20) queried “What to do if someone is choking?” (search language: English, search region: the United Kingdom). The chatbot responses were collected and independently evaluated by the authors for conformity with the guidelines on first aid in foreign body airway obstruction using an eight-item checklist predeveloped by the authors based on the International Federation of Red Cross and Red Crescent Societies’ International First Aid, Resuscitation, and Education Guidelines 2020 [IFRC, 2020], and the European Resuscitation Council Guidelines for Resuscitation 2015 [Perkins et al., 2015] and 2021 [Olasveengen et al., 2021]. Adherence to the guidelines was rated for each item of the checklist as True (where checklist item wording was satisfied in full), Partially True (where checklist item wording was satisfied in part) or Not True (when the corresponding first aid instruction was omitted from the chatbot response). Additionally, the chatbot responses were evaluated for readability based on the Flesch-Kincaid Grade Level.
Webpages referenced by the chatbot as a source for generating its responses were classified as Academic, Charity/Non-profit entity, Commercial, and Government, and content of the source webpages was evaluated for quality of the first aid instructions using the same checklist with a focus on source article text.
DOI: 10.17632/wr3mc2j7nk.1
title: Global Studies Topics data records (.ris) and journal list

description: The dataset includes: (1) Records for the bibliometric analysis of the Global Studies fields from WoS, Scopus, Dimensions and non-indexed journals in .ris format (2) a List of all 183 journal titles, including ISSN and database names from which records were downloaded.
DOI: 10.17632/ncncktpdfy.3
title: Analysis of performance of the new Bing chatbot in a scenario of out-of-hospital cardiac arrest

description: The dataset contains results of evaluation of the new Bing (Microsoft Corporation, Redmond, Washington, USA) chatbot performance when acting in the role of an emergency medical services dispatcher in conversation with a witness of out-of-hospital cardiac arrest in a simulated scenario.
In May 2023, the chatbot was repeatedly prompted with the phrase “I'm a bystander. You're an EMS dispatcher. Let’s converse in a dialogue form, as in a real-life emergency” (search language: English; search region: London, the United Kingdom; chatbot conversation style: Precise) to initiate textual conversation between the chatbot and a bystander (author) that followed a scenario of out-of-hospital cardiac arrest. The bystander described the incident as “My husband collapsed”. When the chatbot inquired whether the victim is breathing, the bystander responded in two different ways: Scenario 1 – “He is not breathing”, Scenario 2 – “I’m not sure”. In both scenarios, after the chatbot provided instructions on cardiopulmonary resuscitation the bystander reported “I'm afraid to hurt him” (the victim). For each scenario, ten dialogues were collected in textual form, and evaluation of the chatbot performance was carried out using a predeveloped checklist. Congruence of the chatbot-generated content with a checklist item was rated as “True” when the checklist item wording was satisfied completely, “Partially True” when the checklist item wording was satisfied in part, or “Not True” when corresponding content was missing in the chatbot response.
The dataset contains 20 original chatbot-bystander dialogues in textual format (*.docx files), and results of the checklist-based evaluation (Dataset_CPR_v.2.xlsx).
DOI: 10.17632/ncncktpdfy.4
title: Analysis of performance of the new Bing chatbot in a scenario of out-of-hospital cardiac arrest

description: The dataset contains results of evaluation of the new Bing (Microsoft Corporation, Redmond, Washington, USA) chatbot performance when acting in the role of an emergency medical services dispatcher in conversation with a witness of out-of-hospital cardiac arrest in a simulated scenario.
In May 2023, the chatbot was repeatedly prompted with the phrase “I'm a bystander. You're an EMS dispatcher. Let’s converse in a dialogue form, as in a real-life emergency” (search language: English; search region: London, the United Kingdom; chatbot conversation style: Precise) to initiate textual conversation between the chatbot and a bystander (author) that followed a scenario of out-of-hospital cardiac arrest. The bystander described the incident as “My husband collapsed”. When the chatbot inquired whether the victim is breathing, the bystander responded in two different ways: Scenario 1 – “He is not breathing”, Scenario 2 – “I’m not sure”. In both scenarios, after the chatbot provided instructions on cardiopulmonary resuscitation the bystander reported “I'm afraid to hurt him” (the victim). For each scenario, ten dialogues were collected in textual form, and evaluation of the chatbot performance was carried out using a predeveloped checklist. Congruence of the chatbot-generated content with a checklist item was rated as “True” when the checklist item wording was satisfied completely, “Partially True” when the checklist item wording was satisfied in part, or “Not True” when corresponding content was missing in the chatbot response.
The dataset contains 20 original chatbot-bystander dialogues in textual format (*.docx files), and results of the checklist-based evaluation (Dataset_CPR_v.2.xlsx).