cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6336114
title: ManyTypes4TypeScript: A Comprehensive TypeScript Dataset for Sequence-Based Type Inference

description: In this paper, we present ManyTypes4TypeScript, a very large corpus for training and evaluating machine-learning models for sequence-based type inference in TypeScript. The dataset includes over 9 million type annotations, across 13,953 projects and 539,571 files. The dataset is approximately 10x larger than analogous type inference datasets for Python, and is the largest available for TypeScript. We also provide API access to the dataset, which can be integrated into any tokenizer and used with any state-of-the-art sequence-based model. Finally, we provide analysis and performance results for state-of-the-art code-specific models, for baselining. ManyTypes4TypeScript is available on Huggingface and Zenodo. This dataset was collected on January 22, 2022 and deduplicated with Allamanis code deduplication tool.
DOI: 10.25919/64wx-0950
title: Chest X-Ray Report Generation Checkpoints for CvT2DistilGPT2

description: The collection is of the checkpoints associated with the paper "Improving Chest X-Ray Report Generation by Leveraging Warm-Starting".
DOI: 10.6084/m9.figshare.14925474.v4
title: Supplementary Materials for "MODELING PSYCHOMETRIC RELATIONAL DATA IN SOCIAL NETWORKS: LATENT INTERDEPENDENCE MODELS"

description: Supplementary materials include empirical data, tables for part of results, and figures from empirical data analysis.
DOI: 10.6084/m9.figshare.14925474
title: Supplementary Materials for "MODELING PSYCHOMETRIC RELATIONAL DATA IN SOCIAL NETWORKS: LATENT INTERDEPENDENCE MODELS"

description: Supplementary materials include empirical data, tables for part of results, and figures from empirical data analysis.
DOI: 10.6084/m9.figshare.14925474.v3
title: Supplementary Materials for "MODELING PSYCHOMETRIC RELATIONAL DATA IN SOCIAL NETWORKS: LATENT INTERDEPENDENCE MODELS"

description: Supplementary materials include empirical data, tables for part of results, and figures from empirical data analysis.
DOI: 10.6084/m9.figshare.19102598.v3
title: ASURAT knowledge-based databases

description: Knowledge-based databases, collected by ASURATDB functions, are stored.<br>
DOI: 10.6084/m9.figshare.15075195
title: MICCAI 2018 – Computational Precision Medicine Challenge: 18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer

description: The Computational Precision Medicine (CPM) 2018 was held on September 16, in Granada (Spain), in conjunction with MICCAI 2018. As part of the CPM program, a series of imaging Grand Challenges were offered, hosted by kaggle. <br><br>This competition, "18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer", was organized as a Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Precision Medicine (CPM) grand challenge. Contestants weretasked to predict, using primary tumor 18F-FDG PET-derived radiomics features +/- matched clinical data, whether a tumor arising from the oropharynx will be controlled by definitive radiation treatment (RT). The head and neck radiation oncology team from University of Texas MD Anderson Cancer Center (MDACC) have curated and harmonized a multi-institutional dataset of 248 oropharynx cancer (OPC) patients, using our in-house 'LAMBDA-RAD' data management platform. Scans came from six different institutes from: the US (MDACC), Canada [four different clinical institutions in Québec: Hôpital Général Juif de Montréal (HGJ), Centre Hospitalier Universitaire de Sherbrooke (CHUS), Centre Hopitalier de l'Université de Montréal (CHUM) and Hôpital Maisonneuve-Rosemont de Montréal (HMR)], and Europe (MAASTRO Clinic, The Netherlands).<br><br>The challenge was open from June 15, 2018, 11:59 p.m. toAug. 30, 2018, midnight UT; this repository serves as FAIR (re)use durable repository for challenge data.<br>Details on the 2018 CPM Challenges can be found at: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224869 <br><br>The "18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer" challenge website (archived) can be viewed at: https://web.archive.org/web/20190106050801/http://miccai.cloudapp.net/competitions/77and at: https://web.archive.org/web/20210418000112/https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224869<br>The kaggle-in-class host page for the challenge and results can be found at: https://www.kaggle.com/c/pet-radiomics-challenges.<br><br><br>
DOI: 10.6084/m9.figshare.15075195.v1
title: MICCAI 2018 – Computational Precision Medicine Challenge: 18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer

description: The Computational Precision Medicine (CPM) 2018 was held on September 16, in Granada (Spain), in conjunction with MICCAI 2018. As part of the CPM program, a series of imaging Grand Challenges were offered, hosted by kaggle. <br><br>This competition, "18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer", was organized as a Medical Image Computing and Computer Assisted Intervention (MICCAI) Computational Precision Medicine (CPM) grand challenge. Contestants weretasked to predict, using primary tumor 18F-FDG PET-derived radiomics features +/- matched clinical data, whether a tumor arising from the oropharynx will be controlled by definitive radiation treatment (RT). The head and neck radiation oncology team from University of Texas MD Anderson Cancer Center (MDACC) have curated and harmonized a multi-institutional dataset of 248 oropharynx cancer (OPC) patients, using our in-house 'LAMBDA-RAD' data management platform. Scans came from six different institutes from: the US (MDACC), Canada [four different clinical institutions in Québec: Hôpital Général Juif de Montréal (HGJ), Centre Hospitalier Universitaire de Sherbrooke (CHUS), Centre Hopitalier de l'Université de Montréal (CHUM) and Hôpital Maisonneuve-Rosemont de Montréal (HMR)], and Europe (MAASTRO Clinic, The Netherlands).<br><br>The challenge was open from June 15, 2018, 11:59 p.m. toAug. 30, 2018, midnight UT; this repository serves as FAIR (re)use durable repository for challenge data.<br>Details on the 2018 CPM Challenges can be found at: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224869 <br><br>The "18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer" challenge website (archived) can be viewed at: https://web.archive.org/web/20190106050801/http://miccai.cloudapp.net/competitions/77<br>The kaggle-in-class host page for the challenge and results can be found at: https://www.kaggle.com/c/pet-radiomics-challenges.<br><br><br>
DOI: 10.5281/zenodo.6327207
title: Analysis of references in the IPCC AR6 WG2 Report of 2022

description: This repository contains data on 17,420 DOIs cited in the IPCC Working Group 2 contribution to the Sixth Assessment Report, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI). References were extracted from the report's PDFs (downloaded 2022-03-01) via Scholarcy and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query. We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard. A brief descriptive analysis was provided as a blogpost on the COKI website. <strong>The repository contains the following content:</strong> Data: <strong>data/scholarcy/RIS/</strong> - extracted references as RIS files <strong>data/scholarcy/BibTeX/</strong> - extracted references as BibTeX files <strong>IPCC_AR6_WGII_dois.csv</strong> - list of DOIs Processing: <strong>preprocessing.txt</strong> - preprocessing steps for identifying and cleaning DOIs <strong>process.py</strong> - Python script for transforming data and linking to COKI data through Google Big Query Outcomes: Dataset on BigQuery - requires a google account for access and bigquery account for querying Data Studio Dashboard - interactive analysis of the generated data Zotero library of references extracted via Scholarcy <strong>PDF version of blogpost</strong> <strong>Note on licenses:</strong> <br> Data are made available under CC0<br> Code is made available under Apache License 2.0
description: Archived version of Release 2022-03-04 of GitHub repository: https://github.com/Curtin-Open-Knowledge-Initiative/ipcc-ar6
DOI: 10.5517/ccdc.csd.cc28k9tz
title: CCDC 2102011: Experimental Crystal Structure Determination

description: Related Article: Teng Fei, Qi Lai, Jinxiong Cai, Jinya Zhang, Chunlin He, Siping Pang|2022|Cryst.Growth Des.|22|1943|doi:10.1021/acs.cgd.1c01487
DOI: 10.5517/ccdc.csd.cc28k9w1
title: CCDC 2102013: Experimental Crystal Structure Determination

description: Related Article: Teng Fei, Qi Lai, Jinxiong Cai, Jinya Zhang, Chunlin He, Siping Pang|2022|Cryst.Growth Des.|22|1943|doi:10.1021/acs.cgd.1c01487
DOI: 10.5517/ccdc.csd.cc28kk8p
title: CCDC 2102242: Experimental Crystal Structure Determination

description: Related Article: Chenxiao Qian, Meiwen Liu, Jianwei Sun, Pengfei Li|2022|Org.Chem.Front.|9|1234|doi:10.1039/D1QO01864G
DOI: 10.5517/ccdc.csd.cc28kkbr
title: CCDC 2102244: Experimental Crystal Structure Determination

description: Related Article: Chenxiao Qian, Meiwen Liu, Jianwei Sun, Pengfei Li|2022|Org.Chem.Front.|9|1234|doi:10.1039/D1QO01864G
DOI: 10.6084/m9.figshare.19320875
title: Additional file 1 of Exploring the mechanisms behind HIV drug resistance in sub-Saharan Africa: conceptual mapping of a complex adaptive system based on multi-disciplinary expert insights

description: Additional file 1. HIVDR as a CAS, visualized in layers. Each element represents a factor influencing HIVDR and each line represents a connection between two factors. Factors are organized in five layers according to their connection with biology, the individual, the social context, the healthcare system and ‘overarching’. A detailed and interactive version of this map is included in Additional file 1 ([27], page 2).
DOI: 10.6084/m9.figshare.19320875.v1
title: Additional file 1 of Exploring the mechanisms behind HIV drug resistance in sub-Saharan Africa: conceptual mapping of a complex adaptive system based on multi-disciplinary expert insights

description: Additional file 1. HIVDR as a CAS, visualized in layers. Each element represents a factor influencing HIVDR and each line represents a connection between two factors. Factors are organized in five layers according to their connection with biology, the individual, the social context, the healthcare system and ‘overarching’. A detailed and interactive version of this map is included in Additional file 1 ([27], page 2).
DOI: 10.6084/m9.figshare.19320890
title: Additional file 3 of Exploring the mechanisms behind HIV drug resistance in sub-Saharan Africa: conceptual mapping of a complex adaptive system based on multi-disciplinary expert insights

description: Additional file 3. Interactive systems map and data set Excel file. Additional file 3 contains a weblink to an online interactive version of the systems maps included in the manuscript as Fig. 2 and Additional file 1. The second and third tab contain the data on the elements, connections, descriptions, connection type, layer category, subsystem category and cluster category necessary to recreate the interactive systems maps in Kumu. Moreover, the code for recreating both the layered view and the subsystem view are provided on tab 4 and 5.
DOI: 10.6084/m9.figshare.19320890.v1
title: Additional file 3 of Exploring the mechanisms behind HIV drug resistance in sub-Saharan Africa: conceptual mapping of a complex adaptive system based on multi-disciplinary expert insights

description: Additional file 3. Interactive systems map and data set Excel file. Additional file 3 contains a weblink to an online interactive version of the systems maps included in the manuscript as Fig. 2 and Additional file 1. The second and third tab contain the data on the elements, connections, descriptions, connection type, layer category, subsystem category and cluster category necessary to recreate the interactive systems maps in Kumu. Moreover, the code for recreating both the layered view and the subsystem view are provided on tab 4 and 5.
DOI: 10.6084/m9.figshare.19320911
title: Additional file 1 of A stakeholder-driven method for selecting implementation strategies: a case example of pediatric hypertension clinical practice guideline implementation

description: Additional file 1. Determinants—Strategies Matrix.
DOI: 10.6084/m9.figshare.19320911.v1
title: Additional file 1 of A stakeholder-driven method for selecting implementation strategies: a case example of pediatric hypertension clinical practice guideline implementation

description: Additional file 1. Determinants—Strategies Matrix.
DOI: 10.6084/m9.figshare.19320892.v1
title: Additional file 2 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 2: Table S1. Summary of Illumina sequencing of Peucedanum.
DOI: 10.6084/m9.figshare.19320892
title: Additional file 2 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 2: Table S1. Summary of Illumina sequencing of Peucedanum.
DOI: 10.6084/m9.figshare.19320904.v1
title: Additional file 4 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 4: Table S3. Codon usage and relative synonymous codon usage (RSCU) values of protein-coding genes of the twelve Peucedanum plastomes.
DOI: 10.6084/m9.figshare.19320904
title: Additional file 4 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 4: Table S3. Codon usage and relative synonymous codon usage (RSCU) values of protein-coding genes of the twelve Peucedanum plastomes.
DOI: 10.6084/m9.figshare.19320907
title: Additional file 5 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 5: Table S4. RNA editing sites detected in the twelve plastome of Peucedanum.
DOI: 10.6084/m9.figshare.19320907.v1
title: Additional file 5 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 5: Table S4. RNA editing sites detected in the twelve plastome of Peucedanum.
DOI: 10.6084/m9.figshare.19320913
title: Additional file 6 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 6: Table S5. Numbers of SSR motifs identified in the twelve Peucedanum plastomes.
DOI: 10.6084/m9.figshare.19320913.v1
title: Additional file 6 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 6: Table S5. Numbers of SSR motifs identified in the twelve Peucedanum plastomes.
DOI: 10.6084/m9.figshare.19320919.v1
title: Additional file 7 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 7: Table S6. Taxa newly sequenced in the present study with source, voucher and GenBank accession numbers.
DOI: 10.6084/m9.figshare.19320919
title: Additional file 7 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 7: Table S6. Taxa newly sequenced in the present study with source, voucher and GenBank accession numbers.
DOI: 10.6084/m9.figshare.19320922
title: Additional file 8 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 8: Table S7. Plastomes included in phylogenetic analyses with GenBank accession.
DOI: 10.6084/m9.figshare.19320922.v1
title: Additional file 8 of The complete plastomes of seven Peucedanum plants: comparative and phylogenetic analyses for the Peucedanum genus

description: Additional file 8: Table S7. Plastomes included in phylogenetic analyses with GenBank accession.
DOI: 10.6084/m9.figshare.19320928
title: Additional file 1 of Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

description: Additional file 1 Supplemental Table 1 – Sample Information. Sample information for all RO-seq libraries used in analyses. Information is as follows: cell type, treatment, time point, enrichment protocol, library preparation method, replicate number, depth, complexity metrics, and SRA identifiers.
DOI: 10.6084/m9.figshare.19320928.v1
title: Additional file 1 of Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

description: Additional file 1 Supplemental Table 1 – Sample Information. Sample information for all RO-seq libraries used in analyses. Information is as follows: cell type, treatment, time point, enrichment protocol, library preparation method, replicate number, depth, complexity metrics, and SRA identifiers.
DOI: 10.6084/m9.figshare.19320943
title: Additional file 1 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 1. MolData Data. The main training dataset consisting of 1.4 million molecules with 600 label columns (all_molecular_data). Moreover, the mapping between the 600 datasets and their corresponding disease (aid_disease_mapping) and target (aid_target_mapping) categories.
DOI: 10.6084/m9.figshare.19320943.v1
title: Additional file 1 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 1. MolData Data. The main training dataset consisting of 1.4 million molecules with 600 label columns (all_molecular_data). Moreover, the mapping between the 600 datasets and their corresponding disease (aid_disease_mapping) and target (aid_target_mapping) categories.
DOI: 10.6084/m9.figshare.19320946
title: Additional file 2 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 2. Data Summary. Summary of all datasets including their AID number, descriptions, statistics, and their disease and target category.
DOI: 10.6084/m9.figshare.19320946.v1
title: Additional file 2 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 2. Data Summary. Summary of all datasets including their AID number, descriptions, statistics, and their disease and target category.
DOI: 10.6084/m9.figshare.19320949
title: Additional file 3 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 3. Additional Reference List. A comprehensive list of references to the PubChem datasets used within MolData as well as their corresponding ChEMBL datasets.
DOI: 10.6084/m9.figshare.19320949.v1
title: Additional file 3 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 3. Additional Reference List. A comprehensive list of references to the PubChem datasets used within MolData as well as their corresponding ChEMBL datasets.
DOI: 10.6084/m9.figshare.19320952
title: Additional file 4 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 4. Interesting Correlations. Interesting linear correlations found between the labels of different datasets.
DOI: 10.6084/m9.figshare.19320952.v1
title: Additional file 4 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 4. Interesting Correlations. Interesting linear correlations found between the labels of different datasets.
DOI: 10.6084/m9.figshare.19320955.v1
title: Additional file 5 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 5. Detailed Results. Detailed results of all models and benchmarks.
DOI: 10.6084/m9.figshare.19320955
title: Additional file 5 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 5. Detailed Results. Detailed results of all models and benchmarks.
DOI: 10.6084/m9.figshare.19320961
title: Additional file 7 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 7. Tagging Keywords. All keywords used for tagging a dataset with a disease benchmark label (i.e., cancer, nervous system, etc.).
DOI: 10.6084/m9.figshare.19320961.v1
title: Additional file 7 of MolData, a molecular benchmark for disease and target based machine learning

description: Additional file 7. Tagging Keywords. All keywords used for tagging a dataset with a disease benchmark label (i.e., cancer, nervous system, etc.).
DOI: 10.6084/m9.figshare.19320998.v1
title: Additional file 1 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

description: Additional file 1. Programs and seeds to simulate data. This file contains the QMSim input file, Fortran programs and seeds used to select the markers and causal loci, to simulate functional effects and genotypes and phenotypic values of new generations, and the interaction matrix used to simulate epistatic effects.
DOI: 10.6084/m9.figshare.19320998
title: Additional file 1 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture

description: Additional file 1. Programs and seeds to simulate data. This file contains the QMSim input file, Fortran programs and seeds used to select the markers and causal loci, to simulate functional effects and genotypes and phenotypic values of new generations, and the interaction matrix used to simulate epistatic effects.
DOI: 10.6084/m9.figshare.19321042.v1
title: Additional file 1 of The microbiome of common bedding materials before and after use on commercial dairy farms

description: Additional file 1: Table S1. Metadata file for all samples included in this study. Table S2. ASVs assigned to each taxonomic level. Table S3. Read counts and mean abundance of assigned species stratified by sample ID. Table S4. Proportion of phylum-level counts, by bedding type and status. Table S5. Proportion of phylum-level counts, for the four non-outlier low-yielding bedding samples. Table S6. Modeling results for associations between bedding status and type, and alpha diversity metrics (LMM output). Table S7. Results from phylum-level differential abundance testing of used versus unused samples, by bedding type. Table S8. Results from class-level differential abundance testing of used versus unused samples, by bedding type. Table S9. Results from genus-level differential abundance testing of used versus unused samples, by bedding type. Table S10. List of potential mastitis pathogens considered in the analysis of 16S rRNA sequence data. Table S11. Proportion of genus-level counts for genera that contain potential mastitis pathogens, by bedding type and status. Table S12. Results from genus-level differential abundance testing of taxa that contain potential mastitis pathogens, comparing used versus unused samples, by bedding type. Table S13. Read counts and proportion of genus-level counts for the mock community and negative control samples.