cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/j33xfgvyyz.2
title: Replication Data for: Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

description: The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly,, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.
DOI: 10.17632/j33xfgvyyz.3
title: Replication Data for: Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

description: The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly,, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.
DOI: 10.17632/j33xfgvyyz.4
title: Replication Data for: Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

description: The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly,, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.
DOI: 10.17632/j33xfgvyyz.5
title: Replication Data for: Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

description: The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly,, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.
DOI: 10.17632/j33xfgvyyz.6
title: Replication Data for: Precision Oncology, Signaling Pathways Reprogramming and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics

description: The TCGA Research Network started in 2005 has profiled and analyzed a large number of human tumors to discover molecular aberrations at the DNA, RNA, protein, and epigenetic levels and thereby provided reliable diagnostic and prognostic biomarkers for different cancer types since then.The presence of mutated genes is strongly correlated with cancer incidence, very specific causative genes or a small set of genes for most cancers have not been confirmed after decades of genomic studies. Nobel laureate James D. Watson opined at Cancer World 2013: "We can go ahead and sequence every piece of DNA that has ever existed, but I don't think we'll find the Achilles heel of cancer. Importantly, it is not only necessary to associate genetic mutations with different cancers but also to work on the mechanism of action of mutagens by focusing on enzymes which could invariably mediate oncogenic transformations. For example, overexpression of the ribonucleotide reductase (RnR) enzyme which catalyzes the formation of deoxyribonucleotides from ribonucleotides necessary for cell division, is implicated in many forms of cancer and the genes for the components of the enzyme are often mutated, leading to hyperactivity of the enzyme. But there are instances indicating that cytoplasmic material rather than the karyoplast would be responsible for cellular transformation that might be better explained as a consequence of certain epigenetic modulation than purely genetic changes. .RnR active site inhibitors have been developed accordingly to biophysically deactivate the enzyme when necessary, with positive results A comprehensive analysis of tumors based on their genomic studies must reveal the alterations in signaling pathways indicating patterns of vulnerabilities and the means to identify prospective targets for the development of personalized treatments and new combination therapies. In this regard, the Cancer Cell Map Initiative (CCMI), launched in 2015 by researchers at the University of California, San Francisco and the University of California, San Diego, allowed researchers to determine how hundreds of genetic mutations involved in a few types of cancer affect the activity of certain crucial proteins which ultimately lead to the manifestation of cancer. As there is a large amount of sequence data from many different cancer types, efforts are being made to extract mechanistic insights from the available information, requiring an integrated computational and experimental strategy that will help place these alterations in the higher order contexts of signaling mechanisms in cancer cells. This is the defined goal of the CCMI and has the potential to create a resource that can be used for cancer genome interpretation, enabling the identification of key complexes and pathways to better understand the biology underlying different cancer types and conditions for precise treatment of the disease.
DOI: 10.15468/dl.zzdfc5
title: Occurrence Download

description: A dataset containing 5721 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhodochorton purpureum (Lightfoot) Rosenvinge"
 ]
}

 The dataset includes 5721 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063839-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sj2th95drf.1
title: Test dataset for 'A 2D cell segmentation protocol for monitoring multiple STAT signaling pathways by fluorescence microscopy' by Szanda et al., STAR Protocols 2024

description: Test images and CellProfiler pipeline for reproducing Figure 4 in https://doi.org/10.1016/j.xpro.2024.103588. See original publication and the description provided for further details.
DOI: 10.17632/c8bs6vp9d3.1
title: (Dataset) Web Accessibility Index vs Latin American Artificial Intelligence Index

description: The "Web Accessibility Index vs Latin American Artificial Intelligence Index" dataset combines metrics on web accessibility in Latin American universities with indicators related to the development and implementation of artificial intelligence (AI) in the region. This dataset enables the analysis of correlations between digital accessibility and AI progress, facilitating the identification of gaps, trends, and areas of opportunity to promote inclusive and sustainable digital transformation in Latin America's academic and technological fields.
DOI: 10.17632/rws8t2pgkb.1
title: Perspectives and Purchase Decisions of Women in their 20s towards Cosmetic Products with “Animal Cruelty-Free” Label

description: This data is to support research that aims to analyze consumer perspectives on cosmetic products labeled "animal cruelty-free" and to raise awareness among Indonesian consumers and cosmetic companies not to conduct animal testing in product development. The data presented is qualitative, expected to be representative so that the results of this study, although all respondents are aware of and feel responsible for the ethical issues surrounding animal testing in cosmetic development, not all sources prioritize the "animal cruelty-free" label in their purchasing decisions. This means that some still prioritize product quality over the ethical efforts behind it.
DOI: 10.17632/899tntr9x2.1
title: Exosomal miR-302b rejuvenates aging mice by reversing the proliferative arrest of senescent cells

description: This dataset contains supplementary Excel tables related to the manuscript "Exosomal miR-302b Rejuvenates Aging Mice by Reversing the Proliferative Arrest of Senescent Cells." The data includes detailed quantitative analyses that support the findings of the study, covering aspects such as gene expression, cytokine levels, cell cycle phases, and the effects of miR-302b treatment on senescent cells and aging tissues.
The dataset comprises the following tables:

Table S1: Bulk RNA-seq data from PDL50 IMR-90 cells treated with hESC-Exos.
Table S2: Cell cycle phase proportions from single-cell RNA sequencing (scRNA-seq) of liver and skin tissues in aging mice.
Table S3: Pseudotemporal analysis of senescent cells transitioning to rejuvenated states.
Table S4: Cytokine profiling in serum samples of aging mice treated with or without hESC-Exos.
Table S5: Annotation of cell types in scRNA-seq data of liver and skin.
Table S6: Differentially expressed genes (DEGs) related to rejuvenation following hESC-Exos treatment.
Table S7: Composition of cell ratios in different experimental groups.
Table S8: Proportions of cells in various phases of the cell cycle.
Table S9: Rescue cell cycle-related genes (CCGs) shared between liver and skin tissues.
Table S10: Pseudotemporal analysis of mitotic cells in liver and skin tissues.
Table S11: Results from Ago2 CLIP-Seq analysis identifying miR-302b target genes.
Table S12: Absolute quantification of miR-302b abundance in different tissues.
Table S13: Bulk RNA-seq data from multiple tissues comparing young, aging, and miR-302b-treated mice.
DOI: 10.17632/899tntr9x2.2
title: Senoreverse candidate: miR-302b

description: This dataset contains supplementary Excel tables related to the manuscript "Exosomal miR-302b Rejuvenates Aging Mice by Reversing the Proliferative Arrest of Senescent Cells." The data includes detailed quantitative analyses that support the findings of the study, covering aspects such as gene expression, cytokine levels, cell cycle phases, and the effects of miR-302b treatment on senescent cells and aging tissues.
The dataset comprises the following tables:

Table S1: Bulk RNA-seq data from PDL50 IMR-90 cells treated with hESC-Exos.
Table S2: Cell cycle phase proportions from single-cell RNA sequencing (scRNA-seq) of liver and skin tissues in aging mice.
Table S3: Pseudotemporal analysis of senescent cells transitioning to rejuvenated states.
Table S4: Cytokine profiling in serum samples of aging mice treated with or without hESC-Exos.
Table S5: Annotation of cell types in scRNA-seq data of liver and skin.
Table S6: Differentially expressed genes (DEGs) related to rejuvenation following hESC-Exos treatment.
Table S7: Composition of cell ratios in different experimental groups.
Table S8: Proportions of cells in various phases of the cell cycle.
Table S9: Rescue cell cycle-related genes (CCGs) shared between liver and skin tissues.
Table S10: Pseudotemporal analysis of mitotic cells in liver and skin tissues.
Table S11: Results from Ago2 CLIP-Seq analysis identifying miR-302b target genes.
Table S12: Absolute quantification of miR-302b abundance in different tissues.
Table S13: Bulk RNA-seq data from multiple tissues comparing young, aging, and miR-302b-treated mice.
DOI: 10.17632/ncz83zrc74.1
title: Wind speed dataset of a wind station in China in September 2020

description: This dataset describes the local wind speed at different altitudes measured by a Chinese wind station in September 2020. Data are recorded in tabular form and values are recorded in ten minutes with maximum, minimum, average and standard deviation of wind speed at altitudes of 100, 90,70,50, 10,8 meters respectively. The data can be collected in real time by the sensor deployed by the team, and the content is detailed and complete with noise interference, which meets the requirements of the experimental data.
DOI: 10.15468/dl.kakf7m
title: Occurrence Download

description: A dataset containing 6799638 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pica pica (Linnaeus, 1758), Canis lupus Linnaeus, 1758)",
 "HasCoordinate is one of (True)"
 ]
}

 The dataset includes 6799638 records from 5806 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063808-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jqbxsk
title: Occurrence Download

description: A dataset containing 339 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Silene wilfordii (Regel) H.Ohashi & H.Nakai"
 ]
}

 The dataset includes 339 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016268-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f428wk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is incertae sedis",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((12.275761 44.613323,12.276389 44.609483,12.276871 44.606215,12.277044 44.603833,12.277362 44.603129,12.277275 44.602401,12.277266 44.602328,12.276143 44.602901,12.275823 44.603064,12.274446 44.604054,12.273995 44.604473,12.2725 44.605865,12.272111 44.60631,12.269711 44.609053,12.267091 44.612529,12.265139 44.615601,12.262289 44.619559,12.261825 44.620413,12.260968 44.622527,12.260811 44.622914,12.2607 44.623188,12.260709 44.6232,12.261034 44.623654,12.263743 44.625404,12.269881 44.629836,12.270287 44.629904,12.270811 44.629992,12.271517 44.629789,12.272161 44.629405,12.272508 44.629212,12.272843 44.628801,12.275103 44.62709,12.275135 44.62676,12.275566 44.626236,12.275967 44.626078,12.27621 44.625852,12.27646 44.625735,12.276652 44.62551,12.277146 44.624289,12.277428 44.622928,12.277362 44.62176,12.276955 44.620894,12.276208 44.619453,12.275695 44.617603,12.275637 44.615666,12.275761 44.613323)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030462-230530130749713/datasets/export for details.