cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mmm7vb99t6.1
title: Supplementary Data for 'Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics'

description: Cancer is a fatal genetic disease involving unregulated cell growth and proliferation with varying underlying complexities including immune evasion, treatment resistance and recurrence, and optimized treatment is required for proper cure. Molecular studies have revealed that tumors are extremely heterogeneous in nature, leading to the complexity of cancer development, which is ultimately linked to its genetic machinery. It would require effective targeting of dysregulated molecular mechanisms involving growth factors, regulatory proteins, cell adhesion molecules, and molecules of immune system mainly driven by alterations in tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same type of cancer respond differently to available cancer treatments, indicating the need for patient-specific treatment options. Thus, in-depth genomic studies of patients’ tumors are needed to fully understand the determinants of cancer initiation and progression for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of tumors to identify molecular alterations involved in cancer manifestation for tailored individualized treatment of the disease. Accordingly, there have been great developments in the formulation and production of anticancer agents in recent years owing to advances in molecular technologies enabling precise targeting of oncogenic pathways involved in disease progression. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of advancements in the tools and techniques associated with the process to assess its scope and importance in realizing the intended goals.
DOI: 10.17632/mmm7vb99t6
title: Supplementary Data for 'The Precision Oncology Approach to Molecular Cancer Therapeutics Targeting Oncogenic Signaling Pathways Is a Means to an End'

description: Cancer is a fatal genetic disease involving unregulated cell growth and proliferation with varying underlying complexities that requires carefully optimized treatment for a full cure. It necessitates effective targeting of dysregulated signaling pathways involving growth factors, regulatory proteins, cell adhesion molecules, and molecules of the immune system, mainly driven by alterations in tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. The application of multi-omics technologies, including single-cell multi-omics, constitutes a novel approach for the identification and quantification of a comprehensive set of biological molecules and to study how they translate into cellular functions and tissue pathologies, which is crucial for precision oncology. Additionally, the role of computational techniques to analyze complex data and identify patterns of disease development to improve outcomes is now well established in medical oncology. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of cutting-edge innovations in tools and techniques associated with the process to assess its scope and importance in achieving the intended goals over time.
DOI: 10.17632/mmm7vb99t6.2
title: Supplementary Data for 'The Precision Oncology Approach to Molecular Cancer Therapeutics Targeting Oncogenic Signaling Pathways Is a Means to an End'

description: Cancer is a fatal genetic disease involving unregulated cell growth and proliferation with varying underlying complexities that requires carefully optimized treatment for a full cure. It necessitates effective targeting of dysregulated signaling pathways involving growth factors, regulatory proteins, cell adhesion molecules, and molecules of the immune system, mainly driven by alterations in tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. The application of multi-omics technologies, including single-cell multi-omics, constitutes a novel approach for the identification and quantification of a comprehensive set of biological molecules and to study how they translate into cellular functions and tissue pathologies, which is crucial for precision oncology. Additionally, the role of computational techniques to analyze complex data and identify patterns of disease development to improve outcomes is now well established in medical oncology. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of cutting-edge innovations in tools and techniques associated with the process to assess its scope and importance in achieving the intended goals over time.
DOI: 10.5281/zenodo.14037972
title: Benthic foraminiferal porosity, oxygen isotope, and bulk elements data of core TN041-8JPC

description: This study reconstructs BWO variations during this period from sediment core TN041-8JPC (17°48.76’N, 57°30.34’E, 761 m water depth) in the western Arabian Sea OMZ, utilizing proxies including benthic foraminiferal surface porosity, redox-sensitive trace metal enrichment factors (e.g., UEF), and U/Ba ratios.
DOI: 10.5281/zenodo.14037973
title: Benthic foraminiferal porosity, oxygen isotope, and bulk elements data of core TN041-8JPC

description: This study reconstructs BWO variations during this period from sediment core TN041-8JPC (17°48.76’N, 57°30.34’E, 761 m water depth) in the western Arabian Sea OMZ, utilizing proxies including benthic foraminiferal surface porosity, redox-sensitive trace metal enrichment factors (e.g., UEF), and U/Ba ratios.
DOI: 10.17632/9dcpdbzv4m
title: Grid population structure simulation of UK Biobank

description: This repository follows the methodology described by and to generate a simulated genetic and phenotypic dataset emulating that of the UK Biobank. 
The tree sequences can be used to generated whole genome sequencing data, along with genotype call.
DOI: 10.17632/9dcpdbzv4m.1
title: Grid population structure simulation of UK Biobank

description: This repository follows the methodology described by and to generate a simulated genetic and phenotypic dataset emulating that of the UK Biobank. 
The tree sequences can be used to generated whole genome sequencing data, along with genotype call.
DOI: 10.15468/dl.qbwnpf
title: Occurrence Download

description: A dataset containing 1140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lophuromys dudui W.Verheyen, Hulselmans, Dierckx & E.Verheyen, 2002",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1140 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033639-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cm9wz2
title: Occurrence Download

description: A dataset containing 503 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((52.05506 -53.77565,215.94494 -53.77565,215.94494 60.77565,52.05506 60.77565,52.05506 -53.77565))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Monostroma nitidum Wittr."
 ]
}

 The dataset includes 503 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033638-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zrv48vh8n4
title: Integrated Kinetic Modeling and Latin Hypercube Sampling-Based Sensitivity Analysis with Bio-oil Characterization in the Pyrolysis of Sargassum sp.

description: Pyrolysis of Sargassum sp. data, including biooil, remaining solid, and gas yields over time
DOI: 10.17632/zrv48vh8n4.1
title: Integrated Kinetic Modeling and Latin Hypercube Sampling-Based Sensitivity Analysis with Bio-oil Characterization in the Pyrolysis of Sargassum sp.

description: Pyrolysis of Sargassum sp. data, including biooil, remaining solid, and gas yields over time
DOI: 10.15468/dl.a9txhf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033640-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15632752
title: 2022 March 28 CME analysis

description: Analysis of the trajectory of the CME event on 2022 March 28.

 

Data from the paper 'Analysis of solar eruptions deflecting in the low corona: influence of the magnetic environment'

If used cite the paper DOI 10.3847/1538-4357/ad96ba
DOI: 10.5281/zenodo.15632753
title: 2022 March 28 CME analysis

description: Analysis of the trajectory of the CME event on 2022 March 28.

 

Data from the paper 'Analysis of solar eruptions deflecting in the low corona: influence of the magnetic environment'

If used cite the paper DOI 10.3847/1538-4357/ad96ba
DOI: 10.17632/tyvfh8gxkb
title: MASH-induced splenic TRNP1+CD8+ T cells exacerbate liver fibrosis

description: The original raw data of 'MASH-induced splenic TRNP1+CD8+ T cells exacerbate liver fibrosis'.
DOI: 10.17632/tyvfh8gxkb.1
title: MASH-induced splenic TRNP1+CD8+ T cells exacerbate liver fibrosis

description: The original raw data of 'MASH-induced splenic TRNP1+CD8+ T cells exacerbate liver fibrosis'.
DOI: 10.17632/43c27d8mgm.1
title: Gene Expression or Mutational Profile for Psoriasis in Guiding Treatment

description: Mendeley Supplementary Table 1. Studies investigating genomic upregulation and downregulation in psoriatic skin lesions.
DOI: 10.17632/43c27d8mgm
title: Gene Expression or Mutational Profile for Psoriasis in Guiding Treatment

description: Mendeley Supplementary Table 1. Studies investigating genomic upregulation and downregulation in psoriatic skin lesions.
DOI: 10.17632/43c27d8mgm.2
title: Gene Expression or Mutational Profile for Psoriasis in Guiding Treatment

description: Mendeley Supplementary Table 1. Studies investigating genomic upregulation and downregulation in psoriatic skin lesions.
DOI: 10.15468/dl.z3kf2a
title: Occurrence Download

description: A dataset containing 2605038 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "HasCoordinate is true",
 "TaxonKey is Agaricomycetes"
 ]
}

 The dataset includes 2605038 records from 306 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033611-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4xq59f
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lophuromys eisentrauti Dieterlen, 1978",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033641-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/5lx4ld
title: Replication Code & Data for "Measurement and Effects of Bank Exit Policies"

description: Replication Code and Data for "Measurement and Effects of Bank Exit Policies" by Daniel Green and Boris Vallee, forthcoming Journal of Financial Economics
DOI: 10.15468/dl.3h3sbu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033642-250525065834625/datasets/export for details.
DOI: 10.17632/wkhg2gnfg4
title: Anexo. Simulación de Modelo de dispersión Pasto

description: Este archivo contiene los datos utilizados y generados en la investigación titulada “Modelación de dispersión de contaminantes atmosféricos generados por motocicletas en el centro de la ciudad de Pasto”. Incluye los cálculos realizados para estimar la dispersión del dióxido de carbono (CO₂) en diferentes escenarios de movilidad urbana mediante la aplicación de un modelo gaussiano.
DOI: 10.17632/wkhg2gnfg4.1
title: Anexo. Simulación de Modelo de dispersión Pasto

description: Este archivo contiene los datos utilizados y generados en la investigación titulada “Modelación de dispersión de contaminantes atmosféricos generados por motocicletas en el centro de la ciudad de Pasto”. Incluye los cálculos realizados para estimar la dispersión del dióxido de carbono (CO₂) en diferentes escenarios de movilidad urbana mediante la aplicación de un modelo gaussiano.
DOI: 10.17632/b2s948425h.1
title: Eucalyptus Leaf Dataset Collected in North Sumatra

description: This dataset comprises images of eucalyptus leaves collected directly from the field laboratory managed by the Center of Excellence in Science and Technology (PUI) Eucalyptus, Universitas Sumatera Utara. It is intended for the development of computer vision-based classification models and includes images of three dominant eucalyptus species commonly found in industrial plantation forests in North Sumatra Province: Eucalyptus grandis, Eucalyptus robusta, and Eucalyptus citriodora.
DOI: 10.17632/b2s948425h
title: Eucalyptus Leaf Dataset Collected in North Sumatra

description: This dataset comprises images of eucalyptus leaves collected directly from the field laboratory managed by the Center of Excellence in Science and Technology (PUI) Eucalyptus, Universitas Sumatera Utara. It is intended for the development of computer vision-based classification models and includes images of three dominant eucalyptus species commonly found in industrial plantation forests in North Sumatra Province: Eucalyptus grandis, Eucalyptus robusta, and Eucalyptus citriodora.
DOI: 10.17632/b2s948425h.2
title: Eucalyptus Leaf Dataset Collected in North Sumatra

description: This dataset comprises images of eucalyptus leaves collected directly from the field laboratory managed by the Center of Excellence in Science and Technology (PUI) Eucalyptus, Universitas Sumatera Utara. It is intended for the development of computer vision-based classification models and includes images of three dominant eucalyptus species commonly found in industrial plantation forests in North Sumatra Province: Eucalyptus grandis, Eucalyptus robusta, and Eucalyptus citriodora.
DOI: 10.17632/tcvbsw3f4y.1
title: Stable isotopic compositions of the Siang-Brahmaputra system

description: Stable isotopic compositions of the Siang-Brahmaputra system
DOI: 10.17632/tcvbsw3f4y.3
title: Stable isotopic compositions of the Siang-Brahmaputra system
DOI: 10.15468/dl.gvummk
title: Occurrence Download

description: A dataset containing 283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ailanthus altissima var. altissima"
 ]
}

 The dataset includes 283 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033643-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/v7tc2xg3k6
title: Terrestrial–aquatic microbial community coalescence slows litter decomposition in streams

description: In the interface between terrestrial and aquatic ecosystems, litter can enter the stream vertically, falling from the tree and into the stream, or laterally, sliding into the stream after transient terrestrial retention. The transition between these ecological contexts can lead to the coalescence of microbial communities. This study focuses on differences in the decomposition between vertical input and lateral input litter, as well as the influence of terrestrial-aquatic microbial community coalescence on their decomposition.
DOI: 10.17632/v7tc2xg3k6.1
title: Terrestrial–aquatic microbial community coalescence slows litter decomposition in streams

description: In the interface between terrestrial and aquatic ecosystems, litter can enter the stream vertically, falling from the tree and into the stream, or laterally, sliding into the stream after transient terrestrial retention. The transition between these ecological contexts can lead to the coalescence of microbial communities. This study focuses on differences in the decomposition between vertical input and lateral input litter, as well as the influence of terrestrial-aquatic microbial community coalescence on their decomposition.
DOI: 10.15468/dl.hmpcn2
title: Occurrence Download

description: A dataset containing 446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nemalion vermiculare Suringar, 1874"
 ]
}

 The dataset includes 446 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033644-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2n4ckjnv3c
title: 4 countries - TEACHERS about Learning with AI - JULY 2024

description: The study employed an exploratory, cross-sectional survey design aimed at understanding how teachers from four countries—Romania, India, China, and Brunei—define, use, and evaluate the effectiveness of artificial intelligence (AI)-based tools in their teaching practice. The research focused on identifying patterns of AI integration across three key phases of instruction (before, during, and after class) and examined their correlation with contextual factors such as teaching experience, professional development, digital infrastructure, and subject area. Data were collected in July 2024 from 252 teachers (68 from Romania, 58 from India, 89 from China, and 37 from Brunei), recruited through academic networks and institutional partnerships using convenience and snowball sampling. Eligible participants were active teachers with regular access to the internet and digital devices; no restrictions were imposed regarding age, gender, or subject specialization. Participation was entirely voluntary and anonymous, with informed consent obtained prior to survey completion. No personal or sensitive data were collected, and given the non-interventional nature of the study and the absence of vulnerable populations, formal ethical approval was not required under national guidelines. Data were gathered via a structured online questionnaire (in English) distributed through Google Forms, comprising 23 items with both closed and open-ended questions organized into sections on demographics, digital infrastructure access, cognitive representations of AI, professional training, practical integration of AI in lessons (including frequency and concrete examples), types of tools used, perceived effectiveness (measured on Likert scales), and barriers reported by non-users. A key finding was that national context significantly shaped AI integration: a Chi² test revealed a statistically significant association between country of origin and the actual use of AI in teaching (χ² = 19.59, df = 6, p = 0.003), suggesting that policy, infrastructure, digital literacy, and professional culture are critical to how educators engage with AI in education.
DOI: 10.17632/2n4ckjnv3c.1
title: 4 countries - TEACHERS about Learning with AI - JULY 2024

description: The study employed an exploratory, cross-sectional survey design aimed at understanding how teachers from four countries—Romania, India, China, and Brunei—define, use, and evaluate the effectiveness of artificial intelligence (AI)-based tools in their teaching practice. The research focused on identifying patterns of AI integration across three key phases of instruction (before, during, and after class) and examined their correlation with contextual factors such as teaching experience, professional development, digital infrastructure, and subject area. Data were collected in July 2024 from 252 teachers (68 from Romania, 58 from India, 89 from China, and 37 from Brunei), recruited through academic networks and institutional partnerships using convenience and snowball sampling. Eligible participants were active teachers with regular access to the internet and digital devices; no restrictions were imposed regarding age, gender, or subject specialization. Participation was entirely voluntary and anonymous, with informed consent obtained prior to survey completion. No personal or sensitive data were collected, and given the non-interventional nature of the study and the absence of vulnerable populations, formal ethical approval was not required under national guidelines. Data were gathered via a structured online questionnaire (in English) distributed through Google Forms, comprising 23 items with both closed and open-ended questions organized into sections on demographics, digital infrastructure access, cognitive representations of AI, professional training, practical integration of AI in lessons (including frequency and concrete examples), types of tools used, perceived effectiveness (measured on Likert scales), and barriers reported by non-users. A key finding was that national context significantly shaped AI integration: a Chi² test revealed a statistically significant association between country of origin and the actual use of AI in teaching (χ² = 19.59, df = 6, p = 0.003), suggesting that policy, infrastructure, digital literacy, and professional culture are critical to how educators engage with AI in education.