cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.zrsfw2
title: Occurrence Download

description: A dataset containing 847 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ithaginis cruentus",
 "Year is one of (1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 847 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011014-251120083545085/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.30728219
title: Comparative Analysis of Microbial Composition in Dental Plaque, Mouthwash, and Tongue Coatings among Non-smoking Women in Their Twenties

description: This dataset contains 16S rRNA gene sequencing data from oral microbiome samples collected from three distinct sites: dental plaque (DQ), mouthwash (MW), and tongue coating (TC) in healthy Korean women in their twenties. The dataset includes ASV abundance tables, taxonomic classifications, representative sequences, phylogenetic trees, and sample metadata. All samples were analyzed using QIIME2 and classified against the SILVA v138.2 database.
DOI: 10.6084/m9.figshare.30728219.v1
title: Comparative Analysis of Microbial Composition in Dental Plaque, Mouthwash, and Tongue Coatings among Non-smoking Women in Their Twenties

description: This dataset contains 16S rRNA gene sequencing data from oral microbiome samples collected from three distinct sites: dental plaque (DQ), mouthwash (MW), and tongue coating (TC) in healthy Korean women in their twenties. The dataset includes ASV abundance tables, taxonomic classifications, representative sequences, phylogenetic trees, and sample metadata. All samples were analyzed using QIIME2 and classified against the SILVA v138.2 database.
DOI: 10.15468/dl.ryd6kq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ixidia cyaniventris",
 "Year is one of (1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011016-251120083545085/datasets/export for details.
DOI: 10.15468/dl.xaxjdy
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trimeresurus medoensis)",
 "DecimalLatitude is greater than or equal to 0",
 "DecimalLatitude is less than or equal to 60",
 "DecimalLongitude is greater than or equal to 60",
 "DecimalLongitude is less than or equal to 150",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 12 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011015-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15700504
title: Empirical Dataset and Computational Implementation of the Hybrid AHP–Entropy–TOPSIS Model for Supplier Selection

description: This repository contains the results of the empirical phase of my dissertation, titled “Hybrid Multicriteria Model Integrated with Business Intelligence to Optimize Supplier Selection.” It includes the dataset zenodo_dataset.csv, which contains the values of five suppliers evaluated according to four criteria (Cost, Quality, Delivery, and Flexibility), as well as the R compilation report of proposed prototype Met_Hybrid_AHP-Entropy-TOPSIS,  which implements a semi-automated version of the hybrid AHP–Entropy–TOPSIS method for optimal supplier selection. Both files enable full reproduction of the example presented in the dissertation (hypothetical case). In addition, a script with execution times and a performance analysis of the model (first simulation) is provided.

A second simulation is also included, used as a cross-validation and comparative analysis with the case of Chen (2020), based on the original data presented in that study (for comparative purposes only). In this second simulation, the model is run again incorporating Chen’s (2020) initial data and performing criterion-by-criterion exclusions (compilation file available), with the aim of analyzing the behavior of the results and generating the data table required for the subsequent sensitivity analysis, which was performed in SPSS (file available).

Legal notice: No license granted. All rights reserved. Copying, modification, or redistribution of any part of this material is prohibited without prior written permission from the author. Materials are made available solely for academic record purposes.
DOI: 10.5281/zenodo.15700505
title: Empirical Dataset and Computational Implementation of the Hybrid AHP–Entropy–TOPSIS Model for Supplier Selection

description: This repository contains the results of the empirical phase of my dissertation, titled “Hybrid Multicriteria Model Integrated with Business Intelligence to Optimize Supplier Selection.” It includes the dataset zenodo_dataset.csv, which contains the values of five suppliers evaluated according to four criteria (Cost, Quality, Delivery, and Flexibility), as well as the R compilation report of proposed prototype Met_Hybrid_AHP-Entropy-TOPSIS,  which implements a semi-automated version of the hybrid AHP–Entropy–TOPSIS method for optimal supplier selection. Both files enable full reproduction of the example presented in the dissertation (hypothetical case). In addition, a script with execution times and a performance analysis of the model (first simulation) is provided.

A second simulation is also included, used as a cross-validation and comparative analysis with the case of Chen (2020), based on the original data presented in that study (for comparative purposes only). In this second simulation, the model is run again incorporating Chen’s (2020) initial data and performing criterion-by-criterion exclusions (compilation file available), with the aim of analyzing the behavior of the results and generating the data table required for the subsequent sensitivity analysis, which was performed in SPSS (file available).

Legal notice: No license granted. All rights reserved. Copying, modification, or redistribution of any part of this material is prohibited without prior written permission from the author. Materials are made available solely for academic record purposes.
DOI: 10.6084/m9.figshare.19386194
title: Transcriptome_COVID19_Cytokine_Mathmodel.xlsx

description: Excel file containing raw transcriptomics data of nasal biopsies from COVID19 infected patients
DOI: 10.6084/m9.figshare.19386194.v1
title: Transcriptome_COVID19_Cytokine_Mathmodel.xlsx

description: Excel file containing raw transcriptomics data of nasal biopsies from COVID19 infected patients
DOI: 10.25919/gr5e-k208
title: Parkes observations for project PX500 semester 2021OCTS_30

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.18739/a2gq6r35w
title: The climate envelope of Alaska’s northern treelines: implications for controlling factors and future treeline advance. Mapping seasonal climate variables 2019 - 2021.

description: Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K (26,000) points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.
DOI: 10.6084/m9.figshare.13530587
title: A dataset with ground reaction forces of human balance in Parkinson's disease

description: The dataset comprises signals from the force platform (raw data for the force, moments of forces, and centers of pressure) of 32 individuals with Parkinson's disease plus one file with information about the patients and balance conditions and the results of the other evaluations. Patient’s balance was evaluated in ON and OFF medication by posturography using a force platform and by the Mini Balance Evaluation Systems Tests. In the posturography test, we evaluated patients during standing still for 30 s in four different conditions where vision and the standing surface were manipulated: on a rigid surface with eyes open; on a rigid surface with eyes closed; on an unstable surface with eyes open; on an unstable surface with eyes closed. Each condition was performed three times and the order of the conditions was randomized among subjects. In addition, the following tests were employed in order to better characterize each patient: Unified Parkinson's disease rating scale motor aspects of experiences of daily living (UPDRS-II) and motor score (UPDRS-III), Hoehn & Yahr (H&Y), New Freezing of Gait Questionnaire (NFOG-Q), Montreal Cognitive Assessment (MoCA), and Falls Efficacy Scale International (FES-I). The patients were also interviewed to collect information about their socio-cultural, demographic, and health characteristics.<br>
DOI: 10.6084/m9.figshare.13530587.v5
title: A dataset with ground reaction forces of human balance in Parkinson's disease

description: The dataset comprises signals from the force platform (raw data for the force, moments of forces, and centers of pressure) of 32 individuals with Parkinson's disease plus one file with information about the patients and balance conditions and the results of the other evaluations. Patient’s balance was evaluated in ON and OFF medication by posturography using a force platform and by the Mini Balance Evaluation Systems Tests. In the posturography test, we evaluated patients during standing still for 30 s in four different conditions where vision and the standing surface were manipulated: on a rigid surface with eyes open; on a rigid surface with eyes closed; on an unstable surface with eyes open; on an unstable surface with eyes closed. Each condition was performed three times and the order of the conditions was randomized among subjects. In addition, the following tests were employed in order to better characterize each patient: Unified Parkinson's disease rating scale motor aspects of experiences of daily living (UPDRS-II) and motor score (UPDRS-III), Hoehn & Yahr (H&Y), New Freezing of Gait Questionnaire (NFOG-Q), Montreal Cognitive Assessment (MoCA), and Falls Efficacy Scale International (FES-I). The patients were also interviewed to collect information about their socio-cultural, demographic, and health characteristics.<br>
DOI: 10.6084/m9.figshare.19380311.v2
title: ICER_2022_Supplemental_Documents_2022-Mar-17-001

description: Supplemental documents for ICER 2022 publication: "Inclusivity Bugs in Online Courseware: A Field Study"
DOI: 10.6084/m9.figshare.19386200
title: Supplementary Data File 1. n=12 Clinical Study: PBMC NAD metabolomes

description: Metabolomics data required for compositonal analysis.<br>
DOI: 10.6084/m9.figshare.19386200.v1
title: Supplementary Data File 1. n=12 Clinical Study: PBMC NAD metabolomes

description: Metabolomics data required for compositonal analysis.<br>
DOI: 10.6084/m9.figshare.19380311.v3
title: ICER_2022_Supplemental_Documents_2022-Mar-17-001

description: Supplemental documents for ICER 2022 publication: "Inclusivity Bugs in Online Courseware: A Field Study"
DOI: 10.6084/m9.figshare.19386242
title: ICER_2022_Supplemental_Documents_2022-Mar-17-001

description: Supplemental documents for ICER 2022 publication: "Inclusivity Bugs in Online Courseware: A Field Study"
DOI: 10.6084/m9.figshare.19386242.v1
title: ICER_2022_Supplemental_Documents_2022-Mar-17-001

description: Supplemental documents for ICER 2022 publication: "Inclusivity Bugs in Online Courseware: A Field Study"
DOI: 10.25345/c5ff3m34w
title: MassIVE Reanalysis RMSV000000360.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080781/PXD004181 [2 out of 3] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.6084/m9.figshare.19384064
title: test_delete

description: test docs
DOI: 10.6084/m9.figshare.19384064.v2
title: test_delete

description: test docs
DOI: 10.6084/m9.figshare.19384064.v1
title: test_delete

description: test docs
DOI: 10.6084/m9.figshare.19380311
title: Test_del

description: test
DOI: 10.6084/m9.figshare.19380311.v5
title: Test_del

description: test
DOI: 10.6084/m9.figshare.19380311.v4
title: ICER_2022_Supplemental_Documents_2022-Mar-17-001

description: Supplemental documents for ICER 2022 publication: "Inclusivity Bugs in Online Courseware: A Field Study"
DOI: 10.18739/a2bz61914
title: The climate envelope of Alaska’s northern treelines: implications for controlling factors and future treeline advance. Primary data and analyses 2019 - 2021

description: Understanding the key mechanisms that control northern treelines is important to accurately predict biome shifts and terrestrial feedbacks to climate. At a global scale, it has long been observed that elevational and latitudinal treelines occur at similar mean growing season air temperature (GSAT) isotherms, inspiring the growth limitation hypothesis (GLH) that cold GSAT limits aboveground growth of treeline trees, with mean treeline GSAT ~6-7 degrees celsius (°C). Treelines with mean GSAT warmer than 6-7 °C may indicate other limiting factors. Many treelines globally are not advancing despite warming, and other climate variables are rarely considered at broad scales. Our goals were to test whether current boreal treelines in northern Alaska correspond with the GLH isotherm, determine which environmental factors are most predictive of treeline presence, and to identify areas beyond the current treeline where advance is most likely. We digitized ~12,400 kilometers (km) of treelines (greater than 26K points) and computed seasonal climate variables across northern Alaska. We then built a generalized additive model predicting treeline presence to identify key factors determining treeline. Two metrics of mean GSAT at Alaska’s northern treelines were consistently warmer than the 6-7 °C isotherm (means of 8.5 °C and 9.3 °C), indicating that direct physiological limitation from low GSAT is unlikely to explain the position of treelines in northern Alaska. Our final model included cumulative growing degree-days, near-surface (≤ 1 meters (m)) permafrost probability, and growing season total precipitation, which together may represent the importance of soil temperature. Our results indicate that mean GSAT may not be the primary driver of treeline in northern Alaska or that its effect is mediated by other more proximate, and possibly non-climatic, controls. Our model predicts treeline potential in several areas beyond current treelines, pointing to possible routes of treeline advance if unconstrained by non-climatic factors.
DOI: 10.5517/ccdc.csd.cc2bbr5m
title: CCDC 2155280: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbrgx
title: CCDC 2155289: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt3m
title: CCDC 2155340: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt4n
title: CCDC 2155341: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt5p
title: CCDC 2155342: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt8s
title: CCDC 2155345: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.25345/c59p2w90r
title: MassIVE Reanalysis RMSV000000362.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000081607 Confetti, GluC-LysC-Elastase HCD [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5251fp2j
title: MassIVE Reanalysis RMSV000000361.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080368/PXD005474 [1 out of 2] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c55x25g6c
title: MassIVE Reanalysis RMSV000000364.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080688/PXD001225 [2 out of 7] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5xg9ff0b
title: MassIVE Reanalysis RMSV000000363.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080644/PXD003966 [1 out of 1] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5sq8qm6k
title: MassIVE Reanalysis RMSV000000365.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080695/PXD002103 [3 out of 3] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5nz80t1b
title: MassIVE Reanalysis RMSV000000366.1 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000086387 A Quantitative Proteome Map of the Human Body, TMT10 MS3 [Mixture: 5] [launched by 109066555a834464860da7ed31006dcf]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.