cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22061207.v6
title: Data from : The macroevolutionary ‘common-garden’ experiment: evolvability of bone organization levels during repeated acquisitions of a slow arboreal lifestyle in mammals

description: - Supplementary Data 1-2: raw data - Supplementary Tables 2-5: analyses results - Supplementary Data 3: time-tree - Supplementary Data 4: R code - Supplementary Information on the methods (Supplementary Table 1 and Notes 1-7), information, credits and copyright licenses for slow arboreal mammal images used in Figs.2 and 4 (Supplementary Note 8) - Details on Extended Data Figs. and Tables are provided in Extended Data Captions.doc
DOI: 10.6084/m9.figshare.22194634
title: Supplementary tables 1-6 for the manuscript by 'Gado et al.'

description: Supplementary Tables 1-6 related to the manuscript by 'Gado et al.,'
DOI: 10.6084/m9.figshare.22194634.v1
title: Supplementary tables 1-6 for the manuscript by 'Gado et al.'

description: Supplementary Tables 1-6 related to the manuscript by 'Gado et al.,'
DOI: 10.5281/zenodo.7689165
title: Migration Data Inventory Records

description: This inventory includes metadata on various quantitative sources of information on migration that can be used for modelling purposes. Documentation: http://www.quantmig.eu/res/QuantMig_documentation_website.pdf Instructional video: https://www.youtube.com/watch?v=Lqm36f0lXho How to cite: <strong>Soto Nishimura A</strong> and <strong>Mooyaart J</strong> (2021) Migration Data Inventory Records: Data Inventory. Online resource, available at http://www.quantmig.eu/data_inventory/.
DOI: 10.5281/zenodo.7689164
title: Migration Data Inventory Records

description: This inventory includes metadata on various quantitative sources of information on migration that can be used for modelling purposes. Documentation: http://www.quantmig.eu/res/QuantMig_documentation_website.pdf Instructional video: https://www.youtube.com/watch?v=Lqm36f0lXho How to cite: <strong>Soto Nishimura A</strong> and <strong>Mooyaart J</strong> (2021) Migration Data Inventory Records: Data Inventory. Online resource, available at http://www.quantmig.eu/data_inventory/.
DOI: 10.5281/zenodo.7689155
title: Migration Drivers Data Inventory Records

description: This inventory includes metadata on various quantitative sources of information on migration drivers that can be used for modelling purposes. Additionally, the inventory includes information on articles that used those quantitative sources such as the statistical effect found in their analysis.
DOI: 10.5281/zenodo.7689154
title: Migration Drivers Data Inventory Records

description: This inventory includes metadata on various quantitative sources of information on migration drivers that can be used for modelling purposes. Additionally, the inventory includes information on articles that used those quantitative sources such as the statistical effect found in their analysis.
DOI: 10.6084/m9.figshare.22196836
title: Furtado ANM et al - @F1000 Res_Supplementary Table 1.xlsx

description: Collections of ectomycorrhizal fungi from restinga fragments on the Brazilian coast. Data highlighted in blue refer to invalid names. All other records are about basidiomata collections. Data included in this table integrate information obtained using the SpeciesLink network (http://splink.cria.org.br), a Brazilian biodiversity database that incorporates data from over 100 mostly Brazilian herbaria. For names of taxa and synonymy, we followed Index Fungorum (http://www.indexfungorum.org/) and MycoBank (http://www.mycobank.org).
DOI: 10.6084/m9.figshare.22196836.v1
title: Furtado ANM et al - @F1000 Res_Supplementary Table 1.xlsx

description: Collections of ectomycorrhizal fungi from restinga fragments on the Brazilian coast. Data highlighted in blue refer to invalid names. All other records are about basidiomata collections. Data included in this table integrate information obtained using the SpeciesLink network (http://splink.cria.org.br), a Brazilian biodiversity database that incorporates data from over 100 mostly Brazilian herbaria. For names of taxa and synonymy, we followed Index Fungorum (http://www.indexfungorum.org/) and MycoBank (http://www.mycobank.org).
DOI: 10.5281/zenodo.7689592
title: scatophagus_argus_CSIRO1_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7689591
title: scatophagus_argus_CSIRO1_ragtag_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7499773
title: Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network

description: This repository contains: openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data. out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.
DOI: 10.5281/zenodo.7499772
title: Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network

description: This repository contains: openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data. out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.
DOI: 10.5281/zenodo.5819061
title: Supplemental Data; "Microbial biominers: sequential bioleaching and biouptake of metals from e-scraps"

description: Acid mine drainage bacterial consortium OTUs reads Metals biouptake from e-scraps leachate normalized by extremotolerant microalgae biomass. For additional information see: García-Balboa C, Martínez-Alesón García P, López-Rodas V, Costas E, Baselga-Cervera B (2022). Microbial biominers: sequential bioleaching and biouptake of metals from electronic scraps. MicrobiologyOpen.
DOI: 10.5281/zenodo.5819060
title: Supplemental Data; "Microbial biominers: sequential bioleaching and biouptake of metals from e-scraps"

description: Acid mine drainage bacterial consortium OTUs reads Metals biouptake from e-scraps leachate normalized by extremotolerant microalgae biomass. For additional information see: García-Balboa C, Martínez-Alesón García P, López-Rodas V, Costas E, Baselga-Cervera B (2022). Microbial biominers: sequential bioleaching and biouptake of metals from electronic scraps. MicrobiologyOpen.
DOI: 10.34740/kaggle/dsv/5089769
title: Dataset, Images of symbols
DOI: 10.5281/zenodo.1472827
title: Civicmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.

This version is up-to-date for roughly June 2018 and is used for the associated paper. More up-to-date versions will follow.
DOI: 10.5281/zenodo.1853547
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.2538900
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.2562671
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.2589442
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.2623379
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.2667312
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.5281/zenodo.3239054
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting.

You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>.

<strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology.

<strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted.

<strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.