cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34816/efa.archives.1135001
title: Délos-PV_3608-Vases géométriques, fragments

description: Référence : PV_3608<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135002
title: Délos-PV_3609-Vase géométrique

description: Référence : PV_3609<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135003
title: Délos-PV_3610-Vases géométriques, fragments

description: Référence : PV_3610<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135004
title: Délos-PV_3611-Vases géométriques

description: Référence : PV_3611<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135005
title: Délos-PV_3612A-Vase

description: Référence : PV_3612A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135006
title: Délos-PV_3612B-Vase

description: Référence : PV_3612B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135007
title: Délos-PV_3613-Vase

description: Référence : PV_3613<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135008
title: Délos-PV_3614A-Vase

description: Référence : PV_3614A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135009
title: Délos-PV_3614B-Vase

description: Référence : PV_3614B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135010
title: Délos-PV_3615-Vase

description: Référence : PV_3615<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135011
title: Délos-PV_3616-Vases

description: Référence : PV_3616<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135012
title: Délos-PV_3617-Vases

description: Référence : PV_3617<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135013
title: Délos-PV_3618-Vases

description: Référence : PV_3618<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135014
title: Délos-PV_3619-Vases, fragments

description: Référence : PV_3619<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135015
title: Délos-PV_3620-Vases, fragments

description: Référence : PV_3620<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135016
title: Délos-PV_3621-Vases, fragments

description: Référence : PV_3621<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135017
title: Délos-PV_3622-Vases, fragments

description: Référence : PV_3622<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135018
title: Délos-PV_3623A-Vase

description: Référence : PV_3623A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135019
title: Délos-PV_3623B-Vases

description: Référence : PV_3623B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135020
title: Délos-PV_3624A-Vase, fragments

description: Référence : PV_3624A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.25430/researchdata.cab.unipd.it.00001518
title: fNIRS & glucose hypoglycemic data of the BabyGlucoLight clinical trial

description: This collection contains data linked to the paper "Investigating brain haemodynamics during hypoglycaemia in very preterm neonates using diffuse optical tomography", that is the glucose data of all the included patients and the .nirs data of all hypoglycemic events described in the paper.
DOI: 10.34816/efa.archives.1135021
title: Délos-PV_3624B-Vase, fragments

description: Référence : PV_3624B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135022
title: Délos-PV_3625A-Vases

description: Référence : PV_3625A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135023
title: Délos-PV_3625B-Vases

description: Référence : PV_3625B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135024
title: Délos-PV_3626-Vases, fragments

description: Référence : PV_3626<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135025
title: Délos-PV_3627-Vases, fragments

description: Référence : PV_3627<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135026
title: Délos-PV_3628A-Vases, fragments

description: Référence : PV_3628A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135027
title: Délos-PV_3628B-Vases, fragments

description: Référence : PV_3628B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135028
title: Délos-PV_3629A-Vases, fragments

description: Référence : PV_3629A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135029
title: Délos-PV_3629B-Vases, fragments

description: Référence : PV_3629B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135030
title: Délos-PV_3630A-Vases, fragments

description: Référence : PV_3630A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.5281/zenodo.13273363
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". 

Training data:



scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. disprot_idr_homologues contain a zip file of IDR homologues corresponding to the DisProt database.

human_protein_alignments contains fasta files for the full proteins containing these IDRs


Weights:



scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. Likewise, disprot_idr_model contains a zip file of our model trained on DisProt IDRs exclusively.


Logo Websites:



scer_idr_logo_website, human_idr_logo_website, and disprot_idr_logo_website contain a zip file of an HTML file for the yeast, human, and DisProt model respectively, showing sequence logos of the features learned by each model and their enrichments.


Features:



human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) 


Classification datasets:



IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin et al. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.5146063
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. It also contains z-scores of all of the model features across all IDRs, which are required to run the mutational scanning map code. <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.5159439
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. It also contains z-scores of all of the model features across all IDRs, which are required to run the mutational scanning map code. <strong>Features:</strong> human_c-conv3_raw_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.6258674
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. <strong>Logo Websites:</strong> scer_idr_logo_website and human_idr_logo_website contain a zip file of an HTML file for the yeast and human model respectively, showing sequence logos of the features learned by each model and their enrichments <strong>Features:</strong> human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.6311384
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. disprot_idr_homologues contain a zip file of IDR homologues corresponding to the DisProt database. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. Likewise, disprot_idr_model contains a zip file of our model trained on DisProt IDRs exclusively. <strong>Logo Websites:</strong> scer_idr_logo_website, human_idr_logo_website, and disprot_idr_logo_website contain a zip file of an HTML file for the yeast, human, and DisProt model respectively, showing sequence logos of the features learned by each model and their enrichments. <strong>Features:</strong> human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.5146062
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". 

Training data:



scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. disprot_idr_homologues contain a zip file of IDR homologues corresponding to the DisProt database.

human_protein_alignments contains fasta files for the full proteins containing these IDRs


Weights:



scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. Likewise, disprot_idr_model contains a zip file of our model trained on DisProt IDRs exclusively.


Logo Websites:



scer_idr_logo_website, human_idr_logo_website, and disprot_idr_logo_website contain a zip file of an HTML file for the yeast, human, and DisProt model respectively, showing sequence logos of the features learned by each model and their enrichments.


Features:



human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) 


Classification datasets:



IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin et al. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.6250565
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. <strong>Features:</strong> human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.5281/zenodo.6250665
title: Data and Weights for Reverse Homology

description: Training data, weights, and classification datasets for "Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning". <strong>Training data:</strong> scer_idr_homologues and human_idr_homologues contain a zip file of the fasta files of IDR homologues used to train the yeast and human model, respectively. Note that these fasta files are aligned, but we strip away the alignment symbol "-" before input into our model. <strong>Weights:</strong> scer_idr_model and human_idr_model contain a zip file of the weights for the yeast and human model respectively, which can be loaded into the model files at github.com/alexxijielu/reverse_homology. <strong>Features:</strong> human_idr_features contains the raw feature activations for all human IDRs in our human model. (We didn't include this file in the supplementary for the paper due to size.) <strong>Classification datasets:</strong> IDR_classification_datasets contains datasets used in our benchmarks. These datasets are encoded as binary csv matrixes. cdc28_classification contains IDRs labeled as Cdc28 phosphorylation sites, mitochondrial_targeting_classification contains IDRs labeled as mitochondrial targeting signals, evosig_cluster_classification contains IDRs labeled by clusters assigned in previous computational work by Zarin <em>et al</em>. eLife 2019, and go_SLIM_classification contains proteins labeled by GO Slim annotations.
DOI: 10.6084/m9.figshare.28633229
title: Arrive Data

description: Arrive Data
DOI: 10.6084/m9.figshare.28633229.v1
title: Arrive Data

description: Arrive Data
DOI: 10.34816/efa.archives.1135031
title: Délos-PV_3630B-Vases, fragments

description: Référence : PV_3630B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135032
title: Délos-PV_3631A-Vase

description: Référence : PV_3631A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135033
title: Délos-PV_3631B-Vase

description: Référence : PV_3631B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135034
title: Délos-PV_3632A-Vase

description: Référence : PV_3632A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135035
title: Délos-PV_3632B-Vase

description: Référence : PV_3632B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135036
title: Délos-PV_3633-Vase

description: Référence : PV_3633<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135037
title: Délos-PV_3634A-Vases

description: Référence : PV_3634A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135038
title: Délos-PV_3634B-Vases

description: Référence : PV_3634B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135039
title: Délos-PV_3635-Vase, fragments

description: Référence : PV_3635<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135040
title: Délos-PV_3636A-Vase

description: Référence : PV_3636A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135041
title: Délos-PV_3636B-Vase

description: Référence : PV_3636B<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.15468/dl.9u8qbn
title: Occurrence Download

description: A dataset containing 115515 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is Canada",
 "TaxonKey is Bubo scandiacus (Linnaeus, 1758)",
 "Year 1970-2025"
 ]
}

 The dataset includes 115515 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018744-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34816/efa.archives.1135042
title: Délos-PV_3637-Vases, fragments

description: Référence : PV_3637<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135043
title: Délos-PV_3638-Vases, fragments

description: Référence : PV_3638<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135044
title: Délos-PV_3639-Vases, fragments

description: Référence : PV_3639<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135045
title: Délos-PV_3640-Vases, fragments

description: Référence : PV_3640<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135047
title: Délos-PV_3642-Vases, fragments

description: Référence : PV_3642<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.15468/dl.gv5fsb
title: Occurrence Download

description: A dataset containing 241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Orchidaceae",
 "Country is Haiti",
 "HasCoordinate is true"
 ]
}

 The dataset includes 241 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018765-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34816/efa.archives.1135048
title: Délos-PV_3643-Vases, fragments

description: Référence : PV_3643<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135049
title: Délos-PV_3644-Vases, fragments

description: Référence : PV_3644<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135050
title: Délos-PV_3645-Vases, fragments

description: Référence : PV_3645<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.7266/ejms49gs
title: Biological collection and tagging of sea turtles in Matagorda Bay, Texas from 2019-08-03 to 2021-07-31
DOI: 10.34816/efa.archives.1135051
title: Délos-PV_3646-Vases, fragments

description: Référence : PV_3646<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.7266/2pn2bb92
title: Invertebrate sampling data from epibenthic sled tows, Matagorda Bay, Texas, 2020-03-03 to 2022-06-15
DOI: 10.34816/efa.archives.1135052
title: Délos-PV_3647A-Vases, fragments

description: Référence : PV_3647A<br />Publication(s) : Inédit<br />Domaine : Vase<br />
DOI: 10.34816/efa.archives.1135053
title: Délos-PV_3647B-Vases, fragments

description: Référence : PV_3647B<br />Publication(s) : Inédit<br />Domaine : Vase<br />