cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26380141.v1
title: Data for Understanding Perception of Implementers on Prime Vendor System in Tanzania Mainland.doc

description: Access to safe, effective, quality and affordable essential medicines for all is a central component of Universal Health Coverage (UHC). However, the availability of quality medicines in low and middle-income countries is often limited, especially in peripheral health facilities. MSD, as the sole supplier of drugs and medical supplies to all public health facilities, has been facing difficulties that hinder its efficiency in supplying 100% of all facility’s needs (HRSC-2017). Despite significant reforms, including introducing the Prime Vendor System in 2018, challenges persist, leading to regional disparities in commodity availability at the facility level. Successfully establishing an intervention based on PPPs within the public sector in the health commodities supply chain system mostly requires high acceptability by the Government, implementers, and the beneficiaries of the intervention (Kumurya, 2015), (Kuwawenaruwa et al., 2020) and (Wiedenmayer et al., 2019). Furthermore, the effectiveness of any activity is primarily influenced by the participants' attitudes. While most studies have extensively researched the effectiveness of the prime vendor system in bridging the supply chain gap, none have researched implementers' perceptions of the prime vendor system in complementing health commodities. This study seeks to understand the Implementer's perceptions of the prime vendor system implementation
DOI: 10.6084/m9.figshare.26391244
title: Underlying Data for article "Altered coagulation profile of patients with high-grade glioma: clinical study with control group"

description: This data set contains sociodemographic and clinical characteristics, ECOG at admission, prothrombin time, activated partial thromboplastin time, international normalized ratio, platelet count, and blood panel results of 21 high-grade glioma patients (cases) and 42 meningioma patients (controls) from northern Peru.
DOI: 10.6084/m9.figshare.26391244.v3
title: Underlying Data for article "Altered coagulation profile of patients with high-grade glioma: clinical study with control group"

description: This data set contains sociodemographic and clinical characteristics, ECOG at admission, prothrombin time, activated partial thromboplastin time, international normalized ratio, platelet count, and blood panel results of 21 high-grade glioma patients (cases) and 42 meningioma patients (controls) from northern Peru.
DOI: 10.15468/dl.vutx2b
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dorstenia astyanactis Aké Assi"
 ]
}

 The dataset includes 80 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042644-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jvzr8c993t.1
title: ЗВІТ про науково-дослідну роботу ЕКОНОМІКО-ПРАВОВІ ЗАСАДИ ІННОВАЦІЙНОГО РОЗВИТКУ МОРСЬКОЇ ГАЛУЗІ

description: Ключові слова: ДЕРЖАВНЕ УПРАВЛІННЯ МОРЕГОСПОДАРСЬКИМ КОМПЛЕКСОМ УКРАЇНИ, ДОГОВІР МОРСЬКОГО СТРАХУВАННЯ, ІННОВАЦІЙНИЙ РОЗВИТОК, МАТРИЦЯ СИСТЕМАТИЗАЦІЇ ПРОБЛЕМ МОРСЬКОЇ ГАЛУЗІ, МОРСЬКІ ВАНТАЖОПЕРЕВЕЗЕННЯ УКРАЇНИ, СТРАТЕГІЯ РОЗВИТКУ ТРАНСПОРТНО-ЛОГІСТИЧНИХ КОМПАНІЙ, ТРАНСПОРТНО-ЛОГІСТИЧНА КОМПАНІЯ.
DOI: 10.6084/m9.figshare.26405800
title: A Computer Vision Approach Towards VerifyingCFD Models of Stirred Tank Reactors [Machine-readable Supporting Information]

description: Zipped folder of machine readable results, ordered according to figures in the paper.
DOI: 10.6084/m9.figshare.26405800.v2
title: A Computer Vision Approach Towards VerifyingCFD Models of Stirred Tank Reactors [Machine-readable Supporting Information]

description: Zipped folder of machine readable results, ordered according to figures in the paper.
DOI: 10.6084/m9.figshare.26405800.v1
title: Supporting Information.zip

description: Zipped folder of machine readable results, ordered according to figures in the paper.
DOI: 10.15468/dl.ajn5w4
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dorstenia dorstenioides (Engl.) Hijman & C.C.Berg"
 ]
}

 The dataset includes 21 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042645-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8sry4728dk.1
title: Life Cycle Assessment of a dental implant: from ore extraction to surgical placement - dataset

description: This dataset is linked to the eponymous manuscript "Life Cycle Assessment of a dental implant: from ore extraction to surgical placement". For further details, please read the manuscript or send an email to the first contributor at sylvain.maladiere.recherche@gmail.com.

The tables compile system inventory flows (per stage, step, and alternative system), and compositions for the modeled flows (e.g. medications).

The tables are composed by "material/process" lines, which are each linked to a process provider in the database. Each flow components are classified per material/process. This makes it easier to compose the system by aggregating flows into sums, linked to provider processes. 

The measures ("Measures") are in the unit of the provider, per item measured. The number ("Nb") referes to the amount of those items used in the table's moment. The coefficient ("Coef") referes to the proportion of flow used. To calculate the quantity of a flow component, do Measures x Nb x Coef.

For flows with a chemical composition (those whose provider names are followed by "conversion"), it was preferable to create their own ingredient proportion tables, named "composition's tables".

In the composition's tables, each ingredient ("Target ingredient") has its proportion in % on the same first line in the "Proportion" column. Some ingredients have a direct provider in the database, and have the same chemical in the "conversion ingredient (proxy)" column. Some do not have a direct associated provider :
- If possible, they are modeled by a individualization in several smaller chemicals which are provided in the database, with their proportion relative to the main chemical
- If a conversion ingredient is not provided in the database, we follow the finechem method applied for LCA and put under the ingredient another chemical as "proxy" preceded by an arrow (which is provided in the database), with a multiplier ratio ; the proxy chemical is multiplied by this ratio to simulate the environmental impact of the conversion chemical.
DOI: 10.17632/8sry4728dk.2
title: Life Cycle Assessment of a dental implant: from ore extraction to surgical placement - dataset

description: This dataset is linked to the eponymous manuscript "Life Cycle Assessment of a dental implant: from ore extraction to surgical placement". For further details, please read the manuscript or send an email to the first contributor at sylvain.maladiere.recherche@gmail.com.

The tables compile system inventory flows (per stage, step, and alternative system), and compositions for the modeled flows (e.g. medications).

The tables are composed by "material/process" lines, which are each linked to a process provider in the database. Each flow components are classified per material/process. This makes it easier to compose the system by aggregating flows into sums, linked to provider processes. 

The measures ("Measures") are in the unit of the provider, per item measured. The number ("Nb") referes to the amount of those items used in the table's moment. The coefficient ("Coef") referes to the proportion of flow used. To calculate the quantity of a flow component, do Measures x Nb x Coef.

For flows with a chemical composition (those whose provider names are followed by "conversion"), it was preferable to create their own ingredient proportion tables, named "composition's tables".

In the composition's tables, each ingredient ("Target ingredient") has its proportion in % on the same first line in the "Proportion" column. Some ingredients have a direct provider in the database, and have the same chemical in the "conversion ingredient (proxy)" column. Some do not have a direct associated provider :
- If possible, they are modeled by a individualization in several smaller chemicals which are provided in the database, with their proportion relative to the main chemical
- If a conversion ingredient is not provided in the database, we follow the finechem method applied for LCA and put under the ingredient another chemical as "proxy" preceded by an arrow (which is provided in the database), with a multiplier ratio ; the proxy chemical is multiplied by this ratio to simulate the environmental impact of the conversion chemical.

V2 : correction of the sensitivity analysis with the reinforced reuse in the Table 33, Table 34 and the Formula 1.
DOI: 10.17632/8sry4728dk.3
title: The need to make surgical placement practices more circular for dental implants: Results of a Life Cycle Assessment study - Dataset

description: This dataset is linked to the eponymous manuscript "Life Cycle Assessment of a dental implant: from ore extraction to surgical placement". For further details, please read the manuscript or send an email to the first contributor at sylvain.maladiere.recherche@gmail.com.

The tables compile system inventory flows (per stage, step, and alternative system), and compositions for the modeled flows (e.g. medications).

The tables are composed by "material/process" lines, which are each linked to a process provider in the database. Each flow components are classified per material/process. This makes it easier to compose the system by aggregating flows into sums, linked to provider processes. 

The measures ("Measures") are in the unit of the provider, per item measured. The number ("Nb") referes to the amount of those items used in the table's moment. The coefficient ("Coef") referes to the proportion of flow used. To calculate the quantity of a flow component, do Measures x Nb x Coef.

For flows with a chemical composition (those whose provider names are followed by "conversion"), it was preferable to create their own ingredient proportion tables, named "composition's tables".

In the composition's tables, each ingredient ("Target ingredient") has its proportion in % on the same first line in the "Proportion" column. Some ingredients have a direct provider in the database, and have the same chemical in the "conversion ingredient (proxy)" column. Some do not have a direct associated provider :
- If possible, they are modeled by a individualization in several smaller chemicals which are provided in the database, with their proportion relative to the main chemical
- If a conversion ingredient is not provided in the database, we follow the finechem method applied for LCA and put under the ingredient another chemical as "proxy" preceded by an arrow (which is provided in the database), with a multiplier ratio ; the proxy chemical is multiplied by this ratio to simulate the environmental impact of the conversion chemical.

V2 : correction of the sensitivity analysis with the reinforced reuse in the Table 33, Table 34 and the Formula 1.

V3 : correction of the title and some table names.
DOI: 10.15468/dl.u4teat
title: Occurrence Download

description: A dataset containing 231 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euproctus platycephalus (Gravenhorst, 1829)"
 ]
}

 The dataset includes 231 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042646-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12608270
title: Quinoacridane[4]arenes – very large conformationally restricted macrocycles
DOI: 10.25810/8196-e820.76
title: United Kingdom Newspaper Coverage of Climate Change or Global Warming, 2000-2024 - June 2024

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.11080113
title: Dataset of the Paper "Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow"

description: The dataset collected from GitHub Discussions, GitHub Issues, and Stack Overflow is used to conduct an empirical study on the problems, causes, and solutions of using GitHub Copilot in practice. A brief description of each document in the dataset is provided below:

1. Dataset(GitHub_Discussions).xlsx

contains the Discussion IDs and URLs in the Copilot category of GitHub Discussions, and the data extracted from the related discussions along with analysis results.

2. Dataset(GitHub_Issues).xlsx

contains the Issue IDs and URLs of the labelled issues which are related to Copilot from GitHub Issues, and the data extracted from the related issues along with analysis results.

3. Dataset(SO_Posts).xlsx

contains the SO Post IDs and URLs of the labelled posts which are related to Copilot from Stack Overflow, and the data extracted from the related posts along with analysis results.

4. Extracted_Data.xlsx

contains the final results of the data extracted from GitHub Discussions, GitHub Issues, and SO posts.

5. pilot labelling folder

contains three .xlsx files (i.e., Pilot_Labelling(GitHub_Discussions).xlsx, Pilot_Labelling(GitHub_Issues), and Pilot_Labelling(SO)), with each file corresponding to one of the three data sources and containing the pilot data labelling results with the Cohen's kappa value.
DOI: 10.5281/zenodo.11080114
title: Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study with Practitioners of GitHub Copilot

description: The dataset collected from GitHub Discussions, GitHub Issues, and Stack Overflow is used to conduct an empirical study on the problems, causes, and solutions of using GitHub Copilot in practice. A brief description of each document in the dataset is provided below:

1. Dataset(GitHub_Discussions).xlsx

contains the Discussion IDs and URLs in the Copilot category of GitHub Discussions, and the data extracted from the related discussions along with analysis results.

2. Dataset(GitHub_Issues).xlsx

contains the Issue IDs and URLs of the labelled issues which are related to Copilot from GitHub Issues, and the data extracted from the related issues along with analysis results.

3. Dataset(SO_Posts).xlsx

contains the SO Post IDs and URLs of the labelled posts which are related to Copilot from Stack Overflow, and the data extracted from the related posts along with analysis results.

4. Extracted_Data.xlsx

contains the final results of the data extracted from GitHub Discussions, GitHub Issues, and SO posts.

5. pilot labelling folder

contains three .xlsx files (i.e., Pilot_Labelling(GitHub_Discussions).xlsx, Pilot_Labelling(GitHub_Issues), and Pilot_Labelling(SO)), with each file corresponding to one of the three data sources and containing the pilot data labelling results with the Cohen's kappa value.
DOI: 10.5281/zenodo.13135695
title: Dataset of the Paper "Exploring the Problems, their Causes and Solutions of AI Pair Programming: A Study on GitHub and Stack Overflow"

description: The dataset collected from GitHub Discussions, GitHub Issues, and Stack Overflow is used to conduct an empirical study on the problems, causes, and solutions of using GitHub Copilot in practice. A brief description of each document in the dataset is provided below:

1. Dataset(GitHub_Discussions).xlsx

contains the Discussion IDs and URLs in the Copilot category of GitHub Discussions, and the data extracted from the related discussions along with analysis results.

2. Dataset(GitHub_Issues).xlsx

contains the Issue IDs and URLs of the labelled issues which are related to Copilot from GitHub Issues, and the data extracted from the related issues along with analysis results.

3. Dataset(SO_Posts).xlsx

contains the SO Post IDs and URLs of the labelled posts which are related to Copilot from Stack Overflow, and the data extracted from the related posts along with analysis results.

4. Extracted_Data.xlsx

contains the final results of the data extracted from GitHub Discussions, GitHub Issues, and SO posts.

5. pilot labelling folder

contains three .xlsx files (i.e., Pilot_Labelling(GitHub_Discussions).xlsx, Pilot_Labelling(GitHub_Issues), and Pilot_Labelling(SO)), with each file corresponding to one of the three data sources and containing the pilot data labelling results with the Cohen's kappa value.
DOI: 10.15468/dl.f9nzgb
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-34.97781 29.67697,-4.24146 29.67697,-4.24146 47.70377,-34.97781 47.70377,-34.97781 29.67697))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Argyropelecus olfersii (Cuvier, 1829)"
 ]
}

 The dataset includes 260 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042648-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jemgjr
title: Occurrence Download

description: A dataset containing 98 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dorstenia involuta Hijman & C.C.Berg"
 ]
}

 The dataset includes 98 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042649-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u82xhz
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is LACM Malacology",
 "TaxonKey is Limidae"
 ]
}

 The dataset includes 123 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042650-240626123714530/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.26405683.v1
title: <b>Systematic Analysis and Mechanistic Exploration of Glyoxalase I Inhibitor Modulation in Breast Cancer-Associated Endothelial Cells: An Integrated Approach of Network Pharmacology, Bioinformatics, and Experimental Validation</b>

description: Breast cancer angiogenesis plays a critical role in the progression of breast cancer. Glyoxalase I (GLO1), while primarily studied for its role in endothelial cells within cardiovascular and metabolic diseases, has an unclear role in breast cancer-associated endothelial cells (BCECs). Additionally, a systematic review of GLO1 inhibitors is lacking. This study aims to systematically analyze the pharmacological profiles of GLO1 inhibitors, screen for those that regulate BCECs, and validate these findings experimentally. The pharmacological profiles of 17 GLO1 inhibitors were analyzed using SwissADME and the pkCSM tool, while target identification was performed using SwissTargetPrediction and SuperPred. A network of potential targets was constructed by intersecting these with differentially expressed genes (DEGs) from the GSE80506 dataset of BCECs. This network was subjected to enrichment analysis, expression correlation analysis, and molecular docking. The results were then validated through cellular assays and aortic ring assay in mice. A total of 515 targets were associated with the 17 GLO1 inhibitors, of which 36 were identified as potential targets related to BCEC regulation. Enrichment analysis revealed that the cell cycle is a critical pathway regulated by GLO1 inhibitors, with CCNA2, CCNE1, CDC25A, CDC25B, CDK6, CHEK1, PLK1, and TTK being key targets. Molecular docking indicated that <i>Glyoxalase I inhibitor 6</i> plays a significant role in regulating these targets. Cellular assays and aortic ring assay in mice demonstrated that this inhibitor blocked cells in the G0/G1 phase by modulating CCNA2 and CCNE1, and effectively inhibited proliferation, migration, and angiogenesis in BCECs, while also promoting cell death. GLO1 inhibitors show potential regulatory effects on BCECs. Specifically, <i>Glyoxalase I inhibitor 6</i> inhibited proliferation, cell cycle progression, migration, and angiogenesis in these cells. These findings suggest a novel approach for utilizing GLO1 inhibitors in targeting tumor-associated endothelial cells.
DOI: 10.6084/m9.figshare.26405683
title: <b>Systematic Analysis and Mechanistic Exploration of Glyoxalase I Inhibitor Modulation in Breast Cancer-Associated Endothelial Cells: An Integrated Approach of Network Pharmacology, Bioinformatics, and Experimental Validation</b>

description: Breast cancer angiogenesis plays a critical role in the progression of breast cancer. Glyoxalase I (GLO1), while primarily studied for its role in endothelial cells within cardiovascular and metabolic diseases, has an unclear role in breast cancer-associated endothelial cells (BCECs). Additionally, a systematic review of GLO1 inhibitors is lacking. This study aims to systematically analyze the pharmacological profiles of GLO1 inhibitors, screen for those that regulate BCECs, and validate these findings experimentally. The pharmacological profiles of 17 GLO1 inhibitors were analyzed using SwissADME and the pkCSM tool, while target identification was performed using SwissTargetPrediction and SuperPred. A network of potential targets was constructed by intersecting these with differentially expressed genes (DEGs) from the GSE80506 dataset of BCECs. This network was subjected to enrichment analysis, expression correlation analysis, and molecular docking. The results were then validated through cellular assays and aortic ring assay in mice. A total of 515 targets were associated with the 17 GLO1 inhibitors, of which 36 were identified as potential targets related to BCEC regulation. Enrichment analysis revealed that the cell cycle is a critical pathway regulated by GLO1 inhibitors, with CCNA2, CCNE1, CDC25A, CDC25B, CDK6, CHEK1, PLK1, and TTK being key targets. Molecular docking indicated that <i>Glyoxalase I inhibitor 6</i> plays a significant role in regulating these targets. Cellular assays and aortic ring assay in mice demonstrated that this inhibitor blocked cells in the G0/G1 phase by modulating CCNA2 and CCNE1, and effectively inhibited proliferation, migration, and angiogenesis in BCECs, while also promoting cell death. GLO1 inhibitors show potential regulatory effects on BCECs. Specifically, <i>Glyoxalase I inhibitor 6</i> inhibited proliferation, cell cycle progression, migration, and angiogenesis in these cells. These findings suggest a novel approach for utilizing GLO1 inhibitors in targeting tumor-associated endothelial cells.
DOI: 10.25810/jck1-hf50.75
title: US Newspaper Coverage of Climate Change or Global Warming, 2000-2024 - June 2024

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.