cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31297803.v1
title: Model building using forward selection and machine learning

description: A common challenge in large-scale data sets with numerous predictors is multicollinearity, which can degrade the performance and interpretability of predictive models. Although dimensionality reduction techniques like Principal Component Analysis (PCA) can mitigate multicollinearity, they transform original variables into new uncorrelated variables, often reducing the model interpretability. For this reason, feature selection techniques are generally preferred in domains where understanding of the original predictors is essential. This paper presents a novel feature selection method that combines machine learning techniques with forward feature selection to effectively address multicollinearity while preserving the accuracy of prediction. The proposed method is evaluated on three diverse data sets from the maritime, healthcare, and environmental domains. Regression models were used to assess predictive performance based on adjusted R2, Root Mean Square Error and Mean Absolute Error. The results demonstrate that the proposed method achieves high performance comparable to traditional feature selection techniques while preventing multicollinearity. This balance between predictive power and feature stability makes it especially suitable for high-dimensional data sets, where redundant or highly correlated variables are prevalent. The approach supports more robust and interpretable models, highlighting its potential for widespread application in complex, data-intensive domains.
DOI: 10.6084/m9.figshare.31297805
title: Advancing the understanding of the immune escape in lung cancer immunotherapy: Global trends, collaborations, and future directions

description: Immune escape mechanisms critically restrict the efficacy of immunotherapy in lung cancer (LC), with only 20%–30% of patients responding to checkpoint inhibitors. To systematically map global trends and identify emerging hotspots, this study analyzed 2813 publications related to immune escape in LC immunotherapy from 1995 to 2025 (data retrieved August 9, 2025; analysis of annual trends uses complete year data through 2024) sourced from the Web of Science Core Collection database. Bibliometric visualization was performed using VOSviewer (version 1.6.19), CiteSpace (version 6.2.R3), and the biblioshiny R package. The dataset encompassed 75 countries, 3421 institutions, and 18,023 researchers across 693 journals. Publication trends delineated three distinct phases: an initial phase (1995–2010), an acceleration phase (2011–2018), and a maturation phase (2019–2024). China dominates in publication volume (1,382 papers) and total citations (45,657), whereas the University of Texas MD Anderson Cancer Center emerged as the most influential institution (n = 87). The <i>Frontiers in Immunology</i> recorded the highest number of publications (n = 155), while <i>Cancer Research</i> received the most co-citations (6,149). Li Zhang ranks as the most prolific author with 19 publications and 1251 citations, while R.S. Herbst holds the highest number of co-citations (n = 628). Current research clusters focus primarily on “therapeutic strategies and treatment integration,” “tumor microenvironment characterization and immune cell dynamics,” and “translational biomarkers and precision immunotherapy.” Future research emphasizes “spatial microenvironment architecture,” “computational predictive modeling,” “alternative cell death pathways,” “precision biomarker development,” “hypoxia-immune interactions,” and “stemness-EMT immune interfaces.” Overall, the field’s exponential growth and geographic disparities provide a structural foundation for developing strategies to overcome immune escape mechanisms by integrating spatial microenvironment characterization with advanced computational modeling.
DOI: 10.6084/m9.figshare.31297805.v1
title: Advancing the understanding of the immune escape in lung cancer immunotherapy: Global trends, collaborations, and future directions

description: Immune escape mechanisms critically restrict the efficacy of immunotherapy in lung cancer (LC), with only 20%–30% of patients responding to checkpoint inhibitors. To systematically map global trends and identify emerging hotspots, this study analyzed 2813 publications related to immune escape in LC immunotherapy from 1995 to 2025 (data retrieved August 9, 2025; analysis of annual trends uses complete year data through 2024) sourced from the Web of Science Core Collection database. Bibliometric visualization was performed using VOSviewer (version 1.6.19), CiteSpace (version 6.2.R3), and the biblioshiny R package. The dataset encompassed 75 countries, 3421 institutions, and 18,023 researchers across 693 journals. Publication trends delineated three distinct phases: an initial phase (1995–2010), an acceleration phase (2011–2018), and a maturation phase (2019–2024). China dominates in publication volume (1,382 papers) and total citations (45,657), whereas the University of Texas MD Anderson Cancer Center emerged as the most influential institution (n = 87). The <i>Frontiers in Immunology</i> recorded the highest number of publications (n = 155), while <i>Cancer Research</i> received the most co-citations (6,149). Li Zhang ranks as the most prolific author with 19 publications and 1251 citations, while R.S. Herbst holds the highest number of co-citations (n = 628). Current research clusters focus primarily on “therapeutic strategies and treatment integration,” “tumor microenvironment characterization and immune cell dynamics,” and “translational biomarkers and precision immunotherapy.” Future research emphasizes “spatial microenvironment architecture,” “computational predictive modeling,” “alternative cell death pathways,” “precision biomarker development,” “hypoxia-immune interactions,” and “stemness-EMT immune interfaces.” Overall, the field’s exponential growth and geographic disparities provide a structural foundation for developing strategies to overcome immune escape mechanisms by integrating spatial microenvironment characterization with advanced computational modeling.
DOI: 10.15468/dl.cdqncy
title: Occurrence Download

description: A dataset containing 356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Panama",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Potamocarcinus armatus, Potamocarcinus magnus, Potamocarcinus richmondi, Ptychophallus barbillaensis, Ptychophallus tumimanus, Ptychophallus kuna, Trichodactylus quinquedentatus, Atya scabra, Atya innocous, Potimirim glabra, Macrobrachium americanum, Macrobrachium carcinus, Macrobrachium digueti, Macrobrachium heterochirus, Macrobrachium panamense, Macrobrachium rosenbergii, Ptychophallus cocleensis, Ptychophallus lavallensis)"
 ]
}

 The dataset includes 356 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002998-260208012135463/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.31297803
title: Model building using forward selection and machine learning

description: A common challenge in large-scale data sets with numerous predictors is multicollinearity, which can degrade the performance and interpretability of predictive models. Although dimensionality reduction techniques like Principal Component Analysis (PCA) can mitigate multicollinearity, they transform original variables into new uncorrelated variables, often reducing the model interpretability. For this reason, feature selection techniques are generally preferred in domains where understanding of the original predictors is essential. This paper presents a novel feature selection method that combines machine learning techniques with forward feature selection to effectively address multicollinearity while preserving the accuracy of prediction. The proposed method is evaluated on three diverse data sets from the maritime, healthcare, and environmental domains. Regression models were used to assess predictive performance based on adjusted R2, Root Mean Square Error and Mean Absolute Error. The results demonstrate that the proposed method achieves high performance comparable to traditional feature selection techniques while preventing multicollinearity. This balance between predictive power and feature stability makes it especially suitable for high-dimensional data sets, where redundant or highly correlated variables are prevalent. The approach supports more robust and interpretable models, highlighting its potential for widespread application in complex, data-intensive domains.
DOI: 10.6084/m9.figshare.31297808
title: Unravelling the diversity of limnic molluscs in the Parnaíba River Basin, Brazil: can ecoregions influence species composition?

description: Molluscs are among the most threatened freshwater organisms in the world; yet, their occurrence and distribution in most ecosystems on Earth remain unknown. This study aimed to survey molluscs in the Parnaíba River Basin, Piauí, Northeastern Brazil, with the goal of understanding the variation in species richness among rivers and the role of ecoregions in their composition. Molluscs associated with macrophytes and sediments were collected from the upstream, middle, and downstream of each of the main rivers in the Basin. Species richness was compared between the rivers and the ecoregions in which they are located. Our study is the first conducted in the Northeast region at the river basin level. It reveals the diversity and complexity of freshwater mollusc assemblages, challenging the idea of low species richness attributed to the Brazilian Northeast and semiarid regions, documenting 10 genera and 12 species of molluscs in the Parnaíba River for the first time. Although no endemic species have been reported for the basin, the species unique to each ecoregion reflect the environmental complexity of these areas. The increasing environmental degradation affecting the region makes it essential to carry out further studies to document the diversity of molluscs in the basin area.
DOI: 10.6084/m9.figshare.31297808.v1
title: Unravelling the diversity of limnic molluscs in the Parnaíba River Basin, Brazil: can ecoregions influence species composition?

description: Molluscs are among the most threatened freshwater organisms in the world; yet, their occurrence and distribution in most ecosystems on Earth remain unknown. This study aimed to survey molluscs in the Parnaíba River Basin, Piauí, Northeastern Brazil, with the goal of understanding the variation in species richness among rivers and the role of ecoregions in their composition. Molluscs associated with macrophytes and sediments were collected from the upstream, middle, and downstream of each of the main rivers in the Basin. Species richness was compared between the rivers and the ecoregions in which they are located. Our study is the first conducted in the Northeast region at the river basin level. It reveals the diversity and complexity of freshwater mollusc assemblages, challenging the idea of low species richness attributed to the Brazilian Northeast and semiarid regions, documenting 10 genera and 12 species of molluscs in the Parnaíba River for the first time. Although no endemic species have been reported for the basin, the species unique to each ecoregion reflect the environmental complexity of these areas. The increasing environmental degradation affecting the region makes it essential to carry out further studies to document the diversity of molluscs in the basin area.
DOI: 10.15468/dl.mvtvea
title: Occurrence Download

description: A dataset containing 1226 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Astrebla squarrosa"
 ]
}

 The dataset includes 1226 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003001-260208012135463/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.31297815
title: Survival outcomes of eyelid lymphoma: a SEER database analysis

description: To evaluate associations between treatment modality and survival in patients with eyelid lymphoma using the SEER national cancer registry, and to identify demographic predictors of prognosis. Data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2021) were analyzed for patients with histologically confirmed eyelid lymphoma. Demographic, histologic, and treatment variables were assessed using Kaplan-Meier and Cox proportional hazards models for 5 year and overall survival respectively. Among 509 cases, extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT; 47.5%) was most common, followed by follicular lymphoma (19.6%) and diffuse large B-cell lymphoma (DLBCL; 12.0%). Radiation therapy was associated with improved survival in patients with MALT lymphoma (<i>p</i> &lt; 0.05) but not in other subtypes. Chemotherapy was not independently associated with improved survival. Increased age at diagnosis and male gender were independent predictors of decreased survival (<i>p</i> &lt; 0.05). Race, income, and marital status were not associated with altered five-year survival. This population-based analysis demonstrates that survival in eyelid lymphoma varies by histologic subtype and is independently associated with age, sex, and radiation therapy use in MALT lymphoma. Findings should be interpreted in light of inherent limitations of registry data, including incomplete treatment and staging information.
DOI: 10.6084/m9.figshare.31297815.v1
title: Survival outcomes of eyelid lymphoma: a SEER database analysis

description: To evaluate associations between treatment modality and survival in patients with eyelid lymphoma using the SEER national cancer registry, and to identify demographic predictors of prognosis. Data from the Surveillance, Epidemiology, and End Results (SEER) database (2000–2021) were analyzed for patients with histologically confirmed eyelid lymphoma. Demographic, histologic, and treatment variables were assessed using Kaplan-Meier and Cox proportional hazards models for 5 year and overall survival respectively. Among 509 cases, extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT; 47.5%) was most common, followed by follicular lymphoma (19.6%) and diffuse large B-cell lymphoma (DLBCL; 12.0%). Radiation therapy was associated with improved survival in patients with MALT lymphoma (<i>p</i> &lt; 0.05) but not in other subtypes. Chemotherapy was not independently associated with improved survival. Increased age at diagnosis and male gender were independent predictors of decreased survival (<i>p</i> &lt; 0.05). Race, income, and marital status were not associated with altered five-year survival. This population-based analysis demonstrates that survival in eyelid lymphoma varies by histologic subtype and is independently associated with age, sex, and radiation therapy use in MALT lymphoma. Findings should be interpreted in light of inherent limitations of registry data, including incomplete treatment and staging information.
DOI: 10.6084/m9.figshare.31297817
title: RNAseq-based meta-analyses revealed tumor suppressor-inducer fusion events in liver, oral, and ovarian cancer in the Indian population: a cancer cell surviving mechanism

description: Cancer cell characteristics are determined by gene expression, influenced by genomic, epigenetic, and transcriptional modifications. Genomic rearrangements and transcriptional splicing can result in the formation of fusion genes. BCR-ABL1 is an established fusion gene employed as a biomarker in leukemia. A single gene can amalgamate with several other genes and may impact cellular fate. Ethnicity-specific variants of fusion genes have been identified, such as the TMPRSS2-ERG variation observed in prostate malignancies among African-American, Caucasian, and Japanese populations in research studies. Next-generation sequencing has provided a new method for predicting genomic and transcriptomic changes. We aim to identify fusion genes in the Indian population using cancer samples to enhance diagnostic outcomes. This study performed a meta-analysis of tumor-specific RNA sequencing data for liver, tongue, and ovarian cancers, which are available online. It identified known fusion genes, including TRO-MAGED2, KRT14-S100A9, RNASE10-CD38, ACTN4-ACTN1, RGPD1-RANBP2, CTSC-RAB38, C15orf57-CBX3, AMBRA1-CKAP5, ATP2B3-ATP2B4, CNKSR3-IPCEF1, E2F4-RPL14, and MZT2A-MZT2B, along with 101 novel fusion genes. Novel fusion genes GABRP_SCGB3A2 and WWOX_FUT1 were identified in all three tumor tissues. GABRP acts as a tumor inducer, whereas SCGB3A2 functions as a tumor suppressor. WWOX2 serves as a tumor suppressor, whereas FUT1 functions as a promoter of malignancy. The interplay between tumor inducers and suppressors may serve as a survival mechanism for cancer cells, a subject that has received limited research attention.
DOI: 10.6084/m9.figshare.31297817.v1
title: RNAseq-based meta-analyses revealed tumor suppressor-inducer fusion events in liver, oral, and ovarian cancer in the Indian population: a cancer cell surviving mechanism

description: Cancer cell characteristics are determined by gene expression, influenced by genomic, epigenetic, and transcriptional modifications. Genomic rearrangements and transcriptional splicing can result in the formation of fusion genes. BCR-ABL1 is an established fusion gene employed as a biomarker in leukemia. A single gene can amalgamate with several other genes and may impact cellular fate. Ethnicity-specific variants of fusion genes have been identified, such as the TMPRSS2-ERG variation observed in prostate malignancies among African-American, Caucasian, and Japanese populations in research studies. Next-generation sequencing has provided a new method for predicting genomic and transcriptomic changes. We aim to identify fusion genes in the Indian population using cancer samples to enhance diagnostic outcomes. This study performed a meta-analysis of tumor-specific RNA sequencing data for liver, tongue, and ovarian cancers, which are available online. It identified known fusion genes, including TRO-MAGED2, KRT14-S100A9, RNASE10-CD38, ACTN4-ACTN1, RGPD1-RANBP2, CTSC-RAB38, C15orf57-CBX3, AMBRA1-CKAP5, ATP2B3-ATP2B4, CNKSR3-IPCEF1, E2F4-RPL14, and MZT2A-MZT2B, along with 101 novel fusion genes. Novel fusion genes GABRP_SCGB3A2 and WWOX_FUT1 were identified in all three tumor tissues. GABRP acts as a tumor inducer, whereas SCGB3A2 functions as a tumor suppressor. WWOX2 serves as a tumor suppressor, whereas FUT1 functions as a promoter of malignancy. The interplay between tumor inducers and suppressors may serve as a survival mechanism for cancer cells, a subject that has received limited research attention.
DOI: 10.60905/hrssnw
title: La universidad perdi�� el a��o

description: &lt;p&gt;Resumen de todos los acontecimientos ocurridos en la UdeA bajo la premisa de que ���1972 fue un a��o negativo para la Universidad oficial en Colombia��� en p��gina completa se resumen los acontecimientos y problem��ticas acaecidas en el tormentoso a��o bajo subt��tulos como: INESTABILIDAD, CO-GOBIERNO, ORDEN INERNO, HUELGAS, DESORDENES, EXPULSIONES, REFORMA UNIVERSITARIA, MAS HUELGAS.&lt;/p&gt;
description: CONTEXTO DE LA NOTICIA: 1972 deja un balance ���satisfactorio��� para Colombia en todos los frentes: Presidente Pastrana
DOI: 10.15468/dl.c7z46k
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tinnea somalensis"
 ]
}

 The dataset includes 29 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003002-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5522/04/31268383
title: Electrical and optical properties of prototype Neuropixels Opto probes

description: This repository contains the data and code (in Matlab) used to analyze the electrical and optical properties of Neuropixels Opto probes, as published inLakunina, Socha, Ladd, et al, “Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics”, <i>Nature Methods</i> 2026The names of the files indicate the relevant figure in the paper:AnalyzeLightPropagation.m (recreates Figure 2b-d)AnalyzeLightInPhantom.m (recreates Extended Data Fig 2b-e)AnalyzeProbeLoss.m (recreates Figure 2a)AnalyzeRMSNoise.m (recreates Extended Data Fig 3a-b)
DOI: 10.5522/04/31268383.v1
title: Electrical and optical properties of prototype Neuropixels Opto probes

description: This repository contains the data and code (in Matlab) used to analyze the electrical and optical properties of Neuropixels Opto probes, as published inLakunina, Socha, Ladd, et al, “Neuropixels Opto: Combining high-resolution electrophysiology and optogenetics”, <i>Nature Methods</i> 2026The names of the files indicate the relevant figure in the paper:AnalyzeLightPropagation.m (recreates Figure 2b-d)AnalyzeLightInPhantom.m (recreates Extended Data Fig 2b-e)AnalyzeProbeLoss.m (recreates Figure 2a)AnalyzeRMSNoise.m (recreates Extended Data Fig 3a-b)
DOI: 10.15468/dl.4eh2jk
title: Occurrence Download

description: A dataset containing 91178 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cypripedium calceolus"
 ]
}

 The dataset includes 91178 records from 533 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002942-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.24n548
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Maihueniopsis crassispina"
 ]
}

 The dataset includes 12 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17938894
title: Battery Imaging Library: Laboratory X-ray nano-CT NMC811 electrode Pristine (reconstructed data)

description: This entry contains the X-ray nano-CT data of a polycrystalline NMC811 acquired at the UCL Centre for Correlative X-ray Microscopy (Department of Chemical Engineering at UCL) using a ZEISS Xradia 810 Ultra. Experiment designed, performed and data acquired by M. P. Jones, F. Iacoviello, A. V. Llewellyn and R. Jervis. A. Vamvakeros did the data curation for BIL.

If you use these data, then please cite the Battery Imaging Library website and the following paper:

Battery Imaging Library: Multi-length scale and multi-modal synchrotron and laboratory battery imaging data for all, R. Docherty et al., 2025
description: Funding: Royal Society Industry Fellowship IF\R2\222059. The Battery Imaging Library is intended for academic and educational purposes. Commercial use should be discussed with the authors.