cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14709005
title: KSFinder 2.0 Data Repository

description: This repository contains data and models developed/generated as part of the project, KSFinder 2.0. The following are the files and a brief description of their contents.



kg_data.zip - (KSFinder 2.0's knowledge graph) This archive contains three folders - kg_train_data.csv, kg_val_data.csv, kg_train_val.csv. a) kg_train_val.csv - This file contains all the triples constituting the knowledge graph (kg); b) This file contains triples that were used train the KGE models and determine optimal hyperparameters; c) kg_val_data.csv - This file contains validation triples that were used to assess the performance of KGE models and determine optimal training epoch.

embeddings.zip - This archive contains embedding data extracted from all the models including the four KGE trained as part of KSFinder 2.0, and embeddings extracted from external models - ProtT5, ESM2, ESM3, ProstT5, Phosformer and a random embedding model.

kg_ks.zip - This archive contains two files a) kinases.csv, a file containing all kg kinases; b) substrates_motif.csv, a file containing all kg substrate_motifs along with their site position, 9-mer and 15-mer motifs.

assessments_data.zip - This archive contains classification datasets used for assessment1, assessment2 and assessment3. It contains subfolders with testing datasets with two different ratio of positives:negatives. a) 1:1 ratio and b) distribution same as training dataset.

ksf2_predictions.zip - This file contains prediction probabilities generated by KSFinder 2.0 for kinase, substrate, motif data.

kge_models_assess1.zip - This file contains the classifier models trained using embeddings from the four KGE models.

models_assess2.zip - This file contains classifier models trained using embeddings from the external models - ProtT5, ESM2, and ESM3.

models_assess3.zip - This file contains classifier models trained to assess the influence of additional features - kinase domain sequences, 15-mer motifs, protein structure based embeddings.

other_model_predictions.zip - This archive contains predictions collected/generated from other kinase-substrate prediction tools, LinkPhinder, PredKinKG, KSFinder, Phosformer-ST.

classifier_datasets.zip - This archive contains the classifier dataset used to train KSFinder 2.0 model. It contains subfolders with testing datasets with two different ratio of positives to negatives. a) 1.1 ratio and b) distribution same as training dataset.

model_ksfinder.zip - This file contains KSFinder 2.0 model.
DOI: 10.5281/zenodo.14713589
title: KSFinder 2.0 Data Repository

description: This repository contains data and models developed/generated as part of the project, KSFinder 2.0. The following are the files and a brief description of their contents.



kg_data.zip - (KSFinder 2.0's knowledge graph) This archive contains three folders - kg_train_data.csv, kg_val_data.csv, kg_train_val.csv. a) kg_train_val.csv - This file contains all the triples constituting the knowledge graph (kg); b) This file contains triples that were used train the KGE models and determine optimal hyperparameters; c) kg_val_data.csv - This file contains validation triples that were used to assess the performance of KGE models and determine optimal training epoch.

embeddings.zip - This archive contains embedding data extracted from all the models including the four KGE trained as part of KSFinder 2.0, and embeddings extracted from external models - ProtT5, ESM2, ESM3, ProstT5, Phosformer and a random embedding model.

kg_ks.zip - This archive contains two files a) kinases.csv, a file containing all kg kinases; b) substrates_motif.csv, a file containing all kg substrate_motifs along with their site position, 9-mer and 15-mer motifs.

assessments_data.zip - This archive contains classification datasets used for assessment1, assessment2 and assessment3. It contains subfolders with testing datasets with two different ratio of positives:negatives. a) 1:1 ratio and b) distribution same as training dataset.

ksf2_predictions.zip - This file contains prediction probabilities generated by KSFinder 2.0 for kinase, substrate, motif data.

kge_models_assess1.zip - This file contains the classifier models trained using embeddings from the four KGE models.

models_assess2.zip - This file contains classifier models trained using embeddings from the external models - ProtT5, ESM2, and ESM3.

models_assess3.zip - This file contains classifier models trained to assess the influence of additional features - kinase domain sequences, 15-mer motifs, protein structure based embeddings.

other_model_predictions.zip - This archive contains predictions collected/generated from other kinase-substrate prediction tools, LinkPhinder, PredKinKG, KSFinder, Phosformer-ST.

classifier_datasets.zip - This archive contains the classifier dataset used to train KSFinder 2.0 model. It contains subfolders with testing datasets with two different ratio of positives to negatives. a) 1.1 ratio and b) distribution same as training dataset.

model_ksfinder.zip - This file contains KSFinder 2.0 model.
DOI: 10.5281/zenodo.15271711
title: KSMoFinder Data Repository

description: This repository contains data and models developed/generated as part of the project, KSMoFinder. The following are the files and a brief description of their contents.



kg_data.zip - (KSMoFinder's knowledge graph) This archive contains three folders - kg_train_data.csv, kg_val_data.csv, kg_train_val.csv. a) kg_train_val.csv - This file contains all the triples constituting the knowledge graph (kg); b) This file contains triples that were used train the KGE models and determine optimal hyperparameters; c) kg_val_data.csv - This file contains validation triples that were used to assess the performance of KGE models and determine optimal training epoch.

embeddings.zip - This archive contains embedding data extracted from all the models including the four KGE trained as part of KSMoFinder, and embeddings extracted from external models - ProtT5, ESM2, ESM3, ProstT5, Phosformer and a random embedding model.

kg_ks.zip - This archive contains two files a) kinases.csv, a file containing all kg kinases; b) substrates_motif.csv, a file containing all kg substrate_motifs along with their site position, 9-mer and 15-mer motifs.

assessments_data.zip - This archive contains classification datasets used for assessment1, assessment2 and assessment3. It contains subfolders with testing datasets with two different ratio of positives:negatives. a) 1:1 ratio and b) distribution same as training dataset.

ksf2_predictions.zip - This file contains prediction probabilities generated by KSMoFinder for kinase, substrate, motif data.

kge_models_assess1.zip - This file contains the classifier models trained using embeddings from the four KGE models.

models_assess2.zip - This file contains classifier models trained using embeddings from the external models - ProtT5, ESM2, and ESM3.

models_assess3.zip - This file contains classifier models trained to assess the influence of additional features - kinase domain sequences, 15-mer motifs, protein structure based embeddings.

other_model_predictions.zip - This archive contains predictions collected/generated from other kinase-substrate prediction tools, LinkPhinder, PredKinKG, KSFinder, Phosformer-ST.

classifier_datasets.zip - This archive contains the classifier dataset used to train the KSMoFinder model. It contains subfolders with testing datasets with two different ratio of positives to negatives. a) 1.1 ratio and b) distribution same as training dataset.

model_ksfinder.zip - This file contains the KSMoFinder model.

other_datafiles.zip - This file contains kinase-group, kinase-family data, motif and substrate protein of phosphosites.
DOI: 10.5281/zenodo.15272558
title: KSMoFinder Data Repository

description: This repository contains data and models developed/generated as part of the project, KSMoFinder. The following are the files and a brief description of their contents.



kg_data.zip - (KSMoFinder's knowledge graph) This archive contains three folders - kg_train_data.csv, kg_val_data.csv, kg_train_val.csv. a) kg_train_val.csv - This file contains all the triples constituting the knowledge graph (kg); b) This file contains triples that were used train the KGE models and determine optimal hyperparameters; c) kg_val_data.csv - This file contains validation triples that were used to assess the performance of KGE models and determine optimal training epoch.

embeddings.zip - This archive contains embedding data extracted from all the models including the four KGE trained as part of KSMoFinder, and embeddings extracted from external models - ProtT5, ESM2, ESM3, ProstT5, Phosformer and a random embedding model.

kg_ks.zip - This archive contains two files a) kinases.csv, a file containing all kg kinases; b) substrates_motif.csv, a file containing all kg substrate_motifs along with their site position, 9-mer and 15-mer motifs.

assessments_data.zip - This archive contains classification datasets used for assessment1, assessment2 and assessment3. It contains subfolders with testing datasets with two different ratio of positives:negatives. a) 1:1 ratio and b) distribution same as training dataset.

ksf2_predictions.zip - This file contains prediction probabilities generated by KSMoFinder for kinase, substrate, motif data.

kge_models_assess1.zip - This file contains the classifier models trained using embeddings from the four KGE models.

models_assess2.zip - This file contains classifier models trained using embeddings from the external models - ProtT5, ESM2, and ESM3.

models_assess3.zip - This file contains classifier models trained to assess the influence of additional features - kinase domain sequences, 15-mer motifs, protein structure based embeddings.

other_model_predictions.zip - This archive contains predictions collected/generated from other kinase-substrate prediction tools, LinkPhinder, PredKinKG, KSFinder, Phosformer-ST.

classifier_datasets.zip - This archive contains the classifier dataset used to train the KSMoFinder model. It contains subfolders with testing datasets with two different ratio of positives to negatives. a) 1.1 ratio and b) distribution same as training dataset.

model_ksfinder.zip - This file contains the KSMoFinder model.

other_datafiles.zip - This file contains kinase-group, kinase-family data, motif and substrate protein of phosphosites.
DOI: 10.3886/e227689
title: State Summaries_Hawaii

description: Veterans as a Percent of County Population (FY2019)<br>VA Facilities (as of 3/30/2021)<br>VA Expenditures FY 2019 (in thousands)<br>Veteran Population (as of 9/30/2019)<br>VA Healthcare and Benefits (as of 9/30/2020)<br>Population Changes<br>Veteran Population Projection By Age and Year<br>Veteran Population Projection By Gender and Year<br>Veteran Population Projection By Period of Service and Year<br>Veteran Population Projection By Race/Ethnicity and Year<br><br>
description: Veterans.
DOI: 10.3886/e227689v1
title: State Summaries_Hawaii

description: Veterans as a Percent of County Population (FY2019)<br>VA Facilities (as of 3/30/2021)<br>VA Expenditures FY 2019 (in thousands)<br>Veteran Population (as of 9/30/2019)<br>VA Healthcare and Benefits (as of 9/30/2020)<br>Population Changes<br>Veteran Population Projection By Age and Year<br>Veteran Population Projection By Gender and Year<br>Veteran Population Projection By Period of Service and Year<br>Veteran Population Projection By Race/Ethnicity and Year<br><br>
description: Veterans.
DOI: 10.3886/e227690
title: VA State Summaries Maine

description: National Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdata<br>Sources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey
DOI: 10.3886/e227690v1
title: VA State Summaries Maine

description: National Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdata<br>Sources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey
DOI: 10.15468/dl.6gy9mm
title: Occurrence Download

description: A dataset containing 1643190 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2010",
 "Month is April",
 "Day is greater than or equal to 1",
 "Day is less than or equal to 14"
 ]
}

 The dataset includes 1643190 records from 399 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000595-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hbp93u
title: Occurrence Download

description: A dataset containing 2070 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-10000.0",
 "Geometry POLYGON((-126.13311 42.06784,-115.71746 42.06784,-115.71746 48.93329,-126.13311 48.93329,-126.13311 42.06784))",
 "TaxonKey is Myocastor coypus (Molina, 1782)"
 ]
}

 The dataset includes 2070 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012195-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q7d595
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dolichopteroides Parin, Belyanina & Evseenko, 2009",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((40.3006 -16.7147,40.6365 -16.1504,35.9648 -16.1504,35.9648 -19.5556,36.0796 -19.4498,36.663 -19.2358,37.5966 -18.1093,38.5624 -17.638,39.3949 -17.5133,39.577 -17.186,40.3006 -16.7147))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008556-250525065834625/datasets/export for details.
DOI: 10.15468/dl.hapxy3
title: Occurrence Download

description: A dataset containing 10961 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Glycaspis aggregata Moore, 1961, Bassariscus astutus (Lichtenstein, 1830), Merimna atrata (Gory & Laporte, 1837), Glycaspis neureta Moore, 1970, Cardiaspina tenuitela Taylor, 1962, Temognatha macfarlani (Waterhouse, 1881), Glycaspis mactans Moore, 1961, Blepharocosta cloezianae Taylor, 1992, Glycaspis wondjinae Moore, 1970, Lymantria albescens, Teratosphaeria considenianae (Crous & Summerell) Crous & Summerell, Isochorista panaeolana Meyrick, 1881, Candovia peridromes (Westwood, 1859), Candovia evoneobertii, Austrocarausius mercurius (Stål, 1877), Acrophylla thoon (Stål, 1877), Cigarrophasma tessellatum Brock & Hasenpusch, 2001, Euchaetis incarnatella (Walker, 1864), Austrocarausius nigropunctatus (Kirby, 1896), Ageletha elaeodes Meyrick, 1883, Acrophylla enceladus Gray, 1835, Austrosipyloidea carterus (Westwood, 1859), Scionecra milledgei Hasenpusch & Brock, 2007, Sipyloidea garradungensis Hasenpusch & Brock, 2007, Anchiale spinicollis (Gray, 1833), Heliocausta oecophorella (Walker, 1864), Temognatha latithorax (Thomson, 1857), Temognatha goryi (Gory & Laporte, 1838), Temognatha marginalis (Carter, 1929), Temognatha spencii (Gory & Laporte, 1838), Temognatha flavomarginata (Gemminger & Harold, 1869), Pseudanilara piliventris Carter, 1926, Palleopa innotata Walker, 1866, Neocuris viridimicans Fairmaire, 1877, Metaxymorpha imitator Neef de Sainval, 1994, Paralaea chionopasta, Paralaea atralba, Melobasis ignipicta Kerremans, 1900, Paralaea sarcodes, Chalcophorotaenia quadriimpressa (Waterhouse, 1875), Castiarina castelnaudi (Saunders, 1869), Castiarina latipes (Carter, 1924), Castiarina pallidipennis (Blackburn, 1890), Castiarina cincta (Blackburn, 1890), Anilara pagana (Obenberger, 1915), Castiarina scintillata (Barker, 1983), Anoplognathus viriditarsis (Leach, 1815), Castiarina acuticollis (Carter, 1916), Spondyliaspis bancrofti Signoret, 1879, Diaspis australis Hempel, 1900)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 10961 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011174-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j2mpen
title: Occurrence Download

description: A dataset containing 164358 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phytolacca americana L."
 ]
}

 The dataset includes 164358 records from 1450 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011559-250515123054153/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.28067639
title: Metadata: NCBI RefSeq accession records for local databases created for SSP

description: These files contain the NCBI RefSeq metadata for the complete genome files downloaded using the script <code>RefSeq_dbBuilder_v3.sh</code>.
DOI: 10.6084/m9.figshare.28067639.v1
title: Metadata: NCBI RefSeq accession records for local databases created for SSP

description: These files contain the NCBI RefSeq metadata for the complete genome files downloaded using the script <code>RefSeq_dbBuilder_v3.sh</code>.
DOI: 10.15468/dl.zte5ut
title: Occurrence Download

description: A dataset containing 58631 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pluchea Cass."
 ]
}

 The dataset includes 58631 records from 678 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002749-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6zdw33
title: Occurrence Download

description: A dataset containing 4377 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Pycnanthemum muticum (Michx.) Pers."
 ]
}

 The dataset includes 4377 records from 159 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002751-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/wm3dgm/vqupad
title: cecr1b-LoF+hrADA2 35 pg (5).tif
DOI: 10.13130/rd_unimi/wm3dgm/1ayrmq
title: image0002.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/29hnps
title: cecr1b-LoF+v (5).tif
DOI: 10.13130/rd_unimi/wm3dgm/y1jitj
title: image0009.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/eiph54
title: cecr1b-LoF+rhADA2 17pg (13).tif
DOI: 10.13130/rd_unimi/wm3dgm/asecgw
title: image0011.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/8a0nma
title: image.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/xyrttb
title: cecr1b-LoF+hrADA2 23 pg (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/ile9iy
title: image0011.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/kjyjpc
title: image0011.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/tytgfx
title: ctrl+v (2).tif
DOI: 10.13130/rd_unimi/wm3dgm/zhpb6x
title: image0002.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/hbgbsa
title: image0004.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/10mulj
title: image0005.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/7n2b95
title: image0003.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/4ig2zq
title: cecr1b-LoF+v (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/rzydnc
title: image0007.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/fkc6cz
title: cecr1b-LoF+v (11).tif
DOI: 10.13130/rd_unimi/wm3dgm/6uoxex
title: image0006.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/nor2jb
title: cecr1b-LoF+hrADA2 35 pg (15).tif
DOI: 10.13130/rd_unimi/wm3dgm/yguoa4
title: cecr1b-LoF+hrADA2 35 pg (14).tif
DOI: 10.13130/rd_unimi/wm3dgm/bvmw46
title: image0001.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/6ccapu
title: image0007.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/crrtbe
title: image0007.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/gonx0g
title: image0006.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/gqamvy
title: image0012.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/d8rb8d
title: image0012.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/ksxgtc
title: cecr1b-LoF+rhADA2 23pg (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/jku3ty
title: image0005.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/luohpr
title: cecr1b-LoF+v (9).tif
DOI: 10.13130/rd_unimi/wm3dgm/62mhnd
title: image0004.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/z2vuq5
title: image.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/i4ypye
title: image.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/3hxqyk
title: ctrl+v (4).tif
DOI: 10.13130/rd_unimi/wm3dgm/t80gux
title: cecr1b-LoF+v (1).tif
DOI: 10.13130/rd_unimi/wm3dgm/1fzvgl
title: image0017.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/y59rgd
title: image0010.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/hftnga
title: cecr1b-LoF+hrADA2 35 pg (10).tif
DOI: 10.13130/rd_unimi/wm3dgm/2voo3p
title: cecr1b-LoF+rhADA2 23pg (1).tif
DOI: 10.13130/rd_unimi/wm3dgm/ieejpo
title: cecr1b-LoF+rhADA2 23pg (17).tif
DOI: 10.13130/rd_unimi/wm3dgm/9yckdf
title: image.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/dnvzuo
title: cecr1b hrADA2 23 pg(6).tif
DOI: 10.13130/rd_unimi/wm3dgm/ctkqg4
title: image0007.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/hrtfsh
title: image0005.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/4nyan5
title: image0001.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/cdbpld
title: image0005.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/1fawlb
title: image0016.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/kjpe74
title: cecr1b-LoF+rhADA2 35pg (5).tif
DOI: 10.13130/rd_unimi/wm3dgm/oiu4dr
title: image0004.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/c5fwyf
title: image0013.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/e1g7es
title: image0003.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/6tobda
title: ctrl+v (3).tif
DOI: 10.13130/rd_unimi/wm3dgm/ohlsjb
title: image0004.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/segrqa
title: image0008.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/bucmgu
title: ctrl+v (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/opjp9p
title: cecr1b-LoF+v (16).tif
DOI: 10.13130/rd_unimi/wm3dgm/pk4fo7
title: image0006.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/ukylhm
title: cecr1b-LoF+hrADA2 35 pg (4).tif
DOI: 10.13130/rd_unimi/wm3dgm/vpvf44
title: image0005.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/v0tja3
title: cecr1b-LoF+v (17).tif
DOI: 10.13130/rd_unimi/wm3dgm/pniwoe
title: cecr1b hrADA2 23 pg(12).tif
DOI: 10.13130/rd_unimi/wm3dgm/skaglq
title: ctrl+v (15).tif
DOI: 10.13130/rd_unimi/wm3dgm/zi9bpa
title: image.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/gmiayu
title: image0004.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/pxq1mj
title: cecr1b-LoF+rhADA2 17pg (14).tif
DOI: 10.13130/rd_unimi/wm3dgm/cp4m77
title: cecr1b-LoF+v (16).tif
DOI: 10.13130/rd_unimi/wm3dgm/rbvuie
title: image0004.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/bucerz
title: image0009.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/3qizmz
title: cecr1b-LoF+v (1).tif
DOI: 10.13130/rd_unimi/wm3dgm/hmwyr7
title: image0011.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/ivlh6m
title: image0009.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/bhifew
title: cecr1b hrADA2 23 pg(1).tif
DOI: 10.13130/rd_unimi/wm3dgm/va2b3r
title: image0007.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/wsnrh2
title: image0001.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/gd79wn
title: cecr1b-LoF+hrADA2 23 pg (3).tif
DOI: 10.13130/rd_unimi/wm3dgm/8dgyse
title: image0006.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/n0ljmj
title: image0001.tif.anx
DOI: 10.13130/rd_unimi/wm3dgm/buhfmm
title: image0008.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/nopvmv
title: image0006.tif.sbx
DOI: 10.13130/rd_unimi/wm3dgm/frs1ab
title: image0005.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/w6ljvb
title: image0014.tif.cal.xml
DOI: 10.13130/rd_unimi/wm3dgm/who3tr
title: image0003.tif.eax
DOI: 10.13130/rd_unimi/wm3dgm/kuntmw
title: image0009.tif.anx