cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7040533
title: RawData_NGS_FTLD

description: A targeted deep sequencing of 98 genes belonging to the lysosomal pathway, selected based on their high expression in multiple brain regions, was performed in 127 subjects (n=74 GRN mutation carriers and n=53 C9orf72 pathological expansion carriers). Sequencing reads were aligned versus the hg19 reference genome using bwa software (mem algorithm, 0.7.17-r1188). Subsequently, duplicated read marking has been performed using Picard and the single-nucleotide variant (SNV) and insertion/deletion (INDEL) calling have been carried out using the Haplotype Caller module of Genome Analysis Toolkit (GATK, version 4.1.8) software over the target region. The Single-Nucleotide Polymorphism Database (dbSNP; v151) was used as the variant reference database. A genomic variant call format (gVCF; version 4.1.8) has been created for each sample. This research was funded by the Italian Ministry of Health, Italy, Ricerca Finalizzata, (grant RF-2016-02361492).
DOI: 10.25574/57360
title: Chandra X-ray Observatory ObsId 57360
DOI: 10.5281/zenodo.7040532
title: RawData_NGS_FTLD

description: A targeted deep sequencing of 98 genes belonging to the lysosomal pathway, selected based on their high expression in multiple brain regions, was performed in 127 subjects (n=74 GRN mutation carriers and n=53 C9orf72 pathological expansion carriers). Sequencing reads were aligned versus the hg19 reference genome using bwa software (mem algorithm, 0.7.17-r1188). Subsequently, duplicated read marking has been performed using Picard and the single-nucleotide variant (SNV) and insertion/deletion (INDEL) calling have been carried out using the Haplotype Caller module of Genome Analysis Toolkit (GATK, version 4.1.8) software over the target region. The Single-Nucleotide Polymorphism Database (dbSNP; v151) was used as the variant reference database. A genomic variant call format (gVCF; version 4.1.8) has been created for each sample. This research was funded by the Italian Ministry of Health, Italy, Ricerca Finalizzata, (grant RF-2016-02361492).
DOI: 10.25574/57364
title: Chandra X-ray Observatory ObsId 57364
DOI: 10.25574/57369
title: Chandra X-ray Observatory ObsId 57369
DOI: 10.25574/57373
title: Chandra X-ray Observatory ObsId 57373
DOI: 10.25574/57377
title: Chandra X-ray Observatory ObsId 57377
DOI: 10.25574/57381
title: Chandra X-ray Observatory ObsId 57381
DOI: 10.25574/57384
title: Chandra X-ray Observatory ObsId 57384
DOI: 10.25574/57388
title: Chandra X-ray Observatory ObsId 57388
DOI: 10.25574/57394
title: Chandra X-ray Observatory ObsId 57394
DOI: 10.25574/57397
title: Chandra X-ray Observatory ObsId 57397
DOI: 10.25574/57399
title: Chandra X-ray Observatory ObsId 57399
DOI: 10.25574/57400
title: Chandra X-ray Observatory ObsId 57400
DOI: 10.25574/57405
title: Chandra X-ray Observatory ObsId 57405
DOI: 10.25574/57413
title: Chandra X-ray Observatory ObsId 57413
DOI: 10.25574/57414
title: Chandra X-ray Observatory ObsId 57414
DOI: 10.25574/57418
title: Chandra X-ray Observatory ObsId 57418
DOI: 10.25574/57420
title: Chandra X-ray Observatory ObsId 57420
DOI: 10.25574/57423
title: Chandra X-ray Observatory ObsId 57423
DOI: 10.25574/57427
title: Chandra X-ray Observatory ObsId 57427
DOI: 10.25574/57429
title: Chandra X-ray Observatory ObsId 57429
DOI: 10.25574/57433
title: Chandra X-ray Observatory ObsId 57433
DOI: 10.25574/57437
title: Chandra X-ray Observatory ObsId 57437
DOI: 10.25574/57440
title: Chandra X-ray Observatory ObsId 57440
DOI: 10.25574/57446
title: Chandra X-ray Observatory ObsId 57446
DOI: 10.25574/57447
title: Chandra X-ray Observatory ObsId 57447
DOI: 10.25574/57451
title: Chandra X-ray Observatory ObsId 57451
DOI: 10.25574/57452
title: Chandra X-ray Observatory ObsId 57452
DOI: 10.25574/57456
title: Chandra X-ray Observatory ObsId 57456
DOI: 10.25574/57458
title: Chandra X-ray Observatory ObsId 57458
DOI: 10.25574/57459
title: Chandra X-ray Observatory ObsId 57459
DOI: 10.25574/57461
title: Chandra X-ray Observatory ObsId 57461
DOI: 10.25574/57464
title: Chandra X-ray Observatory ObsId 57464
DOI: 10.25574/57465
title: Chandra X-ray Observatory ObsId 57465
DOI: 10.25574/57471
title: Chandra X-ray Observatory ObsId 57471
DOI: 10.25574/57473
title: Chandra X-ray Observatory ObsId 57473
DOI: 10.25574/57475
title: Chandra X-ray Observatory ObsId 57475
DOI: 10.25574/57479
title: Chandra X-ray Observatory ObsId 57479
DOI: 10.25574/57481
title: Chandra X-ray Observatory ObsId 57481
DOI: 10.25574/57484
title: Chandra X-ray Observatory ObsId 57484
DOI: 10.25574/57486
title: Chandra X-ray Observatory ObsId 57486
DOI: 10.25574/57491
title: Chandra X-ray Observatory ObsId 57491
DOI: 10.25574/57493
title: Chandra X-ray Observatory ObsId 57493
DOI: 10.25574/57497
title: Chandra X-ray Observatory ObsId 57497
DOI: 10.25574/57499
title: Chandra X-ray Observatory ObsId 57499
DOI: 10.5281/zenodo.6369178
title: Lattice dataset for the paper arXiv:2202.08795 "Simulating rare kaon decays using domain wall lattice QCD with physical light quark masses"

description: Release for https://arxiv.org/abs/2202.08795
description: This project was additionally supported by the DISCnet Centre for Doctoral Training (STFC grant ST/P006760/1).
DOI: 10.5281/zenodo.7011523
title: Demographic information, as well as questionnaire answers

description: Demographic information, as well as questionnaire answers
DOI: 10.6084/m9.figshare.20526864.v1
title: the data of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization

description: the data of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization
DOI: 10.6084/m9.figshare.20526864
title: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization

description: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization
DOI: 10.6084/m9.figshare.20526864.v3
title: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization

description: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization
DOI: 10.6084/m9.figshare.20526864.v2
title: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization

description: the data and code of Robot Fast Peg-In-Hole Assembly Policy Based on Experience Fusion Proximal Policy Optimization
DOI: 10.20372/aau_rdr/n7awdi
title: Magnitude, Awareness and Management of Light Pollution in Bole Sub City, Addis Ababa

description: The data is both qualitative and quantitative measurement's and descriptions about light pollution in Bole Sub City
DOI: 10.34740/kaggle/dsv/4147855
title: 2021 World Population (updated daily)
DOI: 10.21227/06e0-6267
title: Do Deep Learning Models Indeed Understand Software Engineering Tasks?

description: Datasets and code in "Do Deep Learning Models Indeed Understand Software Engineering Tasks?"Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed UnderstandSoftware Engineering Tasks?
DOI: 10.21227/tfr0-2215
title: Do Deep Learning Models Indeed Understand Software Engineering Tasks?

description: Datasets and code in "Do Deep Learning Models Indeed Understand Software Engineering Tasks?"Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed UnderstandSoftware Engineering Tasks?
DOI: 10.21227/wjat-hp04
title: Do Deep Learning Models Indeed Understand Software Engineering Tasks?

description: Datasets and code in "Do Deep Learning Models Indeed Understand Software Engineering Tasks?"Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed UnderstandSoftware Engineering Tasks?
DOI: 10.21227/zfj1-a834
title: Do Deep Learning Models Indeed Understand Software Engineering Tasks?

description: Datasets and code in "Do Deep Learning Models Indeed Understand Software Engineering Tasks?"Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed Understand Software Engineering Tasks?Do Deep Learning Models Indeed UnderstandSoftware Engineering Tasks?
DOI: 10.5281/zenodo.7011522
title: Demographic information, as well as questionnaire answers

description: Demographic information, as well as questionnaire answers
DOI: 10.5281/zenodo.7013418
title: Drone images of dolphins

description: Drone images of dolphins obtained during focal follows in Israel and Italy. The footage is arranged by species (striped dolphins, <em>Stenella coeruleoalba;</em> common bottlenose dolphins, <em>Tursiops truncatus</em>; and short-beaked common dolphin, <em>Delphinus delphis</em>) and sea state (0-2 on the Beaufort scale). Further information including the metadata for each image is available in: Bigal, E.; Galili, O.; van Rijn, I.; Rosso, M.; Cleguer, C.; Hodgson, A.; Scheinin, A.; Tchernov, D. Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs). <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 4118. https://doi.org/10.3390/rs14164118
DOI: 10.5281/zenodo.7013417
title: Drone images of dolphins

description: Drone images of dolphins obtained during focal follows in Israel and Italy. The footage is arranged by species (striped dolphins, <em>Stenella coeruleoalba;</em> common bottlenose dolphins, <em>Tursiops truncatus</em>; and short-beaked common dolphin, <em>Delphinus delphis</em>) and sea state (0-2 on the Beaufort scale). Further information including the metadata for each image is available in: Bigal, E.; Galili, O.; van Rijn, I.; Rosso, M.; Cleguer, C.; Hodgson, A.; Scheinin, A.; Tchernov, D. Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs). <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>, 4118. https://doi.org/10.3390/rs14164118
DOI: 10.5519/qd.q54d4yxz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (625 records)
DOI: 10.6084/m9.figshare.14573538
title: Deposit for "Siblings matter: Family heterogeneity improves associative learning later in life"

description: This deposit includes all the data and R-code for the analysis presented in the main manuscript as well as the supplementary file. <br>
DOI: 10.5281/zenodo.7034189
title: The title (test) for ReLTER

description: This is the description of the dataset.
DOI: 10.5281/zenodo.7034190
title: The title (test) for ReLTER

description: This is the description of the dataset.
DOI: 10.5281/zenodo.7041152
title: The title (test) for ReLTER

description: This is the description of the dataset.
DOI: 10.6084/m9.figshare.20462565.v2
title: Insulin prevents fatty acid induced increase of adipocyte size

description: Metabolic disorders related to obesity are largely dependent on adipose tissue hypertrophy, which involves adipocyte hypertrophy and increased adipogenesis. Adiposize is regulated by lipid accumulation as a result of increased lipogenesis (mainly lipid uptake in mature adipocytes) and reduced lipolysis. Using realtime 2D cell culture analyses of lipid uptake, we show (1) that high glucose concentration (4.5 g/L) was required to accumulate oleic acid increasing lipid droplet size until unilocularization similar to mature adipocytes in few days, (2) oleic acid reduced <i>Peroxisome-Proliferator Activated Receptor Gamma</i> (<i>PPARG)</i> gene transcription and (3) insulin counteracted oleic acid-induced increase of lipid droplet size. Although the lipolytic activity observed in high <i>versus</i> low glucose (1 g/L) conditions was not altered, insulin was found to inhibit oleic acid induced gene transcription required for lipid storage such as Cell Death Inducing DFFA Like Effectors (CIDEC) and <i>G0S2 (</i>G0 switch gene S2), possibly through PPARA activity. Although this signalling pathway requires more detailed investigation, the results point out the differential mechanisms involved in the pro-adipogenic effect of insulin in absence <i>versus</i> its protective effect on adiposity in presence of oleic acid uptake. <b>Abbreviations</b>: AICAR, 5-Aminoimidazole-4-carboxamide-1-D-ribofuranoside; AMPK, AMP-Activated protein kinase, ASCs, adipose stem cell; ATGL, adipose triglyceride lipase; BSA, Bovine serum albumin; CEBPA, CCAAT enhancer binding protein alpha; CIDEs, Cell Death Inducing DFFA Like Effectors; dA, differentiated adipocyte; DMEM, Dulbecco’s Modified Eagle’s Medium; FABPs, Fatty Acid Binding Proteins; FAT/CD36, Fatty acid translocase; FCS, Foetal calf serum; FN1, fibronectin 1; FFA, free fatty acid; G0S2, G0 switch gene S2; GLUTs, Glucose transporters; GPR120, G protein-coupled receptor 120; HG, high glucose; HSL, hormone sensitive lipase; INSR, insulin receptor; LG, low glucose; OA, oleic acid; PBS, Phosphate buffer saline; PPARs, Peroxisome-Proliferator Activated Receptors; PKA, Protein kinase cyclic AMP-dependent; PKG, Protein kinase cyclic GMP dependent; PTGS2, cytochrome oxidase 2; RTCA, realtime cell analysis; TG, triglyceride.
DOI: 10.5281/zenodo.6998080
title: Spirals inside the millimeter cavity of transition disk SR 21

description: This is a basic reproduction package for the paper 'Shadowing and multiple rings in the protoplanetary disk of HD 139614' by Muro-Arena et al (2022). It aims to provide the most important data products to check and reproduce the main results of the paper, listing all software used and data archives containing the public data used. It is available at arXiv: https://ui.adsabs.harvard.edu/abs/2020A%26A...636L...4M/abstract and has been published by Astronomy and Astrophysics at https://www.aanda.org/articles/aa/full_html/2020/04/aa37656-20/aa37656-20.html
DOI: 10.5281/zenodo.6998081
title: Spirals inside the millimeter cavity of transition disk SR 21

description: This is a basic reproduction package for the paper 'Shadowing and multiple rings in the protoplanetary disk of HD 139614' by Muro-Arena et al (2022). It aims to provide the most important data products to check and reproduce the main results of the paper, listing all software used and data archives containing the public data used. It is available at arXiv: https://ui.adsabs.harvard.edu/abs/2020A%26A...636L...4M/abstract and has been published by Astronomy and Astrophysics at https://www.aanda.org/articles/aa/full_html/2020/04/aa37656-20/aa37656-20.html
DOI: 10.5281/zenodo.6998061
title: Shadowing and multiple rings in the protoplanetary disk of HD 139614

description: This is a basic reproduction package for the paper 'Shadowing and multiple rings in the protoplanetary disk of HD 139614' by Muro-Arena et al (2022). It aims to provide the most important data products to check and reproduce the main results of the paper, listing all software used and data archives containing the public data used. It is available at - arXiv: https://ui.adsabs.harvard.edu/abs/2020A%26A...635A.121M/abstract and has been published by Astronomy and Astrophysics at https://www.aanda.org/articles/aa/full_html/2020/03/aa36509-19/aa36509-19.html
DOI: 10.5281/zenodo.6998062
title: Shadowing and multiple rings in the protoplanetary disk of HD 139614

description: This is a basic reproduction package for the paper 'Shadowing and multiple rings in the protoplanetary disk of HD 139614' by Muro-Arena et al (2022). It aims to provide the most important data products to check and reproduce the main results of the paper, listing all software used and data archives containing the public data used. It is available at - arXiv: https://ui.adsabs.harvard.edu/abs/2020A%26A...635A.121M/abstract and has been published by Astronomy and Astrophysics at https://www.aanda.org/articles/aa/full_html/2020/03/aa36509-19/aa36509-19.html
DOI: 10.25574/57502
title: Chandra X-ray Observatory ObsId 57502
DOI: 10.25574/57505
title: Chandra X-ray Observatory ObsId 57505
DOI: 10.25574/57507
title: Chandra X-ray Observatory ObsId 57507
DOI: 10.25574/57510
title: Chandra X-ray Observatory ObsId 57510
DOI: 10.25574/57515
title: Chandra X-ray Observatory ObsId 57515
DOI: 10.25574/57518
title: Chandra X-ray Observatory ObsId 57518
DOI: 10.25574/57521
title: Chandra X-ray Observatory ObsId 57521
DOI: 10.25574/57522
title: Chandra X-ray Observatory ObsId 57522
DOI: 10.25574/57526
title: Chandra X-ray Observatory ObsId 57526
DOI: 10.25574/57528
title: Chandra X-ray Observatory ObsId 57528
DOI: 10.25574/57530
title: Chandra X-ray Observatory ObsId 57530
DOI: 10.25574/57532
title: Chandra X-ray Observatory ObsId 57532
DOI: 10.25574/57533
title: Chandra X-ray Observatory ObsId 57533
DOI: 10.25574/57535
title: Chandra X-ray Observatory ObsId 57535
DOI: 10.25574/57536
title: Chandra X-ray Observatory ObsId 57536
DOI: 10.25574/57538
title: Chandra X-ray Observatory ObsId 57538
DOI: 10.25574/57539
title: Chandra X-ray Observatory ObsId 57539
DOI: 10.25574/57541
title: Chandra X-ray Observatory ObsId 57541
DOI: 10.25574/57542
title: Chandra X-ray Observatory ObsId 57542
DOI: 10.25574/57545
title: Chandra X-ray Observatory ObsId 57545
DOI: 10.25574/57546
title: Chandra X-ray Observatory ObsId 57546
DOI: 10.25574/57548
title: Chandra X-ray Observatory ObsId 57548
DOI: 10.25574/57551
title: Chandra X-ray Observatory ObsId 57551
DOI: 10.25574/57553
title: Chandra X-ray Observatory ObsId 57553
DOI: 10.25574/57555
title: Chandra X-ray Observatory ObsId 57555
DOI: 10.25574/57558
title: Chandra X-ray Observatory ObsId 57558
DOI: 10.25574/57559
title: Chandra X-ray Observatory ObsId 57559
DOI: 10.25574/57561
title: Chandra X-ray Observatory ObsId 57561
DOI: 10.25574/57565
title: Chandra X-ray Observatory ObsId 57565
DOI: 10.25574/57567
title: Chandra X-ray Observatory ObsId 57567
DOI: 10.25574/57571
title: Chandra X-ray Observatory ObsId 57571
DOI: 10.25574/57572
title: Chandra X-ray Observatory ObsId 57572
DOI: 10.25574/57574
title: Chandra X-ray Observatory ObsId 57574
DOI: 10.25574/57575
title: Chandra X-ray Observatory ObsId 57575
DOI: 10.25574/57579
title: Chandra X-ray Observatory ObsId 57579
DOI: 10.25574/57589
title: Chandra X-ray Observatory ObsId 57589
DOI: 10.25574/57590
title: Chandra X-ray Observatory ObsId 57590
DOI: 10.25574/57593
title: Chandra X-ray Observatory ObsId 57593
DOI: 10.25574/57594
title: Chandra X-ray Observatory ObsId 57594
DOI: 10.25574/57597
title: Chandra X-ray Observatory ObsId 57597
DOI: 10.25574/57598
title: Chandra X-ray Observatory ObsId 57598
DOI: 10.25574/57600
title: Chandra X-ray Observatory ObsId 57600
DOI: 10.25574/57601
title: Chandra X-ray Observatory ObsId 57601
DOI: 10.25574/57603
title: Chandra X-ray Observatory ObsId 57603
DOI: 10.25574/57605
title: Chandra X-ray Observatory ObsId 57605
DOI: 10.25574/57607
title: Chandra X-ray Observatory ObsId 57607
DOI: 10.25574/57608
title: Chandra X-ray Observatory ObsId 57608
DOI: 10.25574/57610
title: Chandra X-ray Observatory ObsId 57610
DOI: 10.25574/57611
title: Chandra X-ray Observatory ObsId 57611
DOI: 10.25574/57614
title: Chandra X-ray Observatory ObsId 57614
DOI: 10.25574/57615
title: Chandra X-ray Observatory ObsId 57615
DOI: 10.25574/57618
title: Chandra X-ray Observatory ObsId 57618
DOI: 10.25574/57619
title: Chandra X-ray Observatory ObsId 57619
DOI: 10.25574/57623
title: Chandra X-ray Observatory ObsId 57623
DOI: 10.25574/57624
title: Chandra X-ray Observatory ObsId 57624
DOI: 10.25574/57628
title: Chandra X-ray Observatory ObsId 57628
DOI: 10.25574/57629
title: Chandra X-ray Observatory ObsId 57629
DOI: 10.25574/57630
title: Chandra X-ray Observatory ObsId 57630
DOI: 10.25574/57631
title: Chandra X-ray Observatory ObsId 57631
DOI: 10.25574/57633
title: Chandra X-ray Observatory ObsId 57633
DOI: 10.25574/57635
title: Chandra X-ray Observatory ObsId 57635
DOI: 10.25574/57636
title: Chandra X-ray Observatory ObsId 57636
DOI: 10.25574/57638
title: Chandra X-ray Observatory ObsId 57638
DOI: 10.25574/57641
title: Chandra X-ray Observatory ObsId 57641
DOI: 10.25574/57642
title: Chandra X-ray Observatory ObsId 57642
DOI: 10.25574/57645
title: Chandra X-ray Observatory ObsId 57645
DOI: 10.25574/57646
title: Chandra X-ray Observatory ObsId 57646
DOI: 10.25574/57649
title: Chandra X-ray Observatory ObsId 57649
DOI: 10.25574/57650
title: Chandra X-ray Observatory ObsId 57650
DOI: 10.25574/57653
title: Chandra X-ray Observatory ObsId 57653
DOI: 10.25574/57655
title: Chandra X-ray Observatory ObsId 57655
DOI: 10.6084/m9.figshare.20768020
title: CERLTurtles-Aug2022-analysisFile

description: This file contains the raw data used for analysis in our manuscript in review in the Journal of Thermal Biology (article identifiers coming soon)
DOI: 10.6084/m9.figshare.20768020.v1
title: CERLTurtles-Aug2022-analysisFile

description: This file contains the raw data used for analysis in our manuscript in review in the Journal of Thermal Biology (article identifiers coming soon)
DOI: 10.25574/57656
title: Chandra X-ray Observatory ObsId 57656
DOI: 10.25574/57659
title: Chandra X-ray Observatory ObsId 57659
DOI: 10.25574/57660
title: Chandra X-ray Observatory ObsId 57660
DOI: 10.25574/57663
title: Chandra X-ray Observatory ObsId 57663
DOI: 10.25574/57664
title: Chandra X-ray Observatory ObsId 57664
DOI: 10.25574/57667
title: Chandra X-ray Observatory ObsId 57667
DOI: 10.25574/57668
title: Chandra X-ray Observatory ObsId 57668
DOI: 10.25574/57671
title: Chandra X-ray Observatory ObsId 57671
DOI: 10.25574/57672
title: Chandra X-ray Observatory ObsId 57672
DOI: 10.25574/57675
title: Chandra X-ray Observatory ObsId 57675
DOI: 10.25574/57676
title: Chandra X-ray Observatory ObsId 57676
DOI: 10.25574/57678
title: Chandra X-ray Observatory ObsId 57678
DOI: 10.25574/57679
title: Chandra X-ray Observatory ObsId 57679
DOI: 10.25574/57681
title: Chandra X-ray Observatory ObsId 57681
DOI: 10.25574/57682
title: Chandra X-ray Observatory ObsId 57682
DOI: 10.25574/57684
title: Chandra X-ray Observatory ObsId 57684
DOI: 10.25574/57685
title: Chandra X-ray Observatory ObsId 57685
DOI: 10.15454/kew474/bg9sez
title: PRUAV_Regina_V1.gff3

description: Preliminary structural annotation of prunus avium 'Regina' genome V1
DOI: 10.15454/kew474/zagtwn
title: PRUAV_Regina_V1.fasta.gz

description: Fasta sequence of the 8 pseudomolecules of prunus avium 'Regina' (PAV01_REGINA -&gt; PAV08_REGINA), 19 non placed scaffolds (SCA_XX) and 907 non placed contigs.
DOI: 10.15454/kew474/cqhwod
title: Poster_Regina_Assembly_PAG2019.pdf

description: Le Dantec L, Girollet N, Gouzy J, Sallet E, Fouché M, Quero-Garcia J, Dirlewanger E (2019) An Improved Assembly of the Diploid ‘Regina’ Sweet Cherry Genome. International Plant & Animal Genome XXVII / January 12-16, 2019 - San Diego, CA, USA.
DOI: 10.25574/57687
title: Chandra X-ray Observatory ObsId 57687
DOI: 10.25574/57688
title: Chandra X-ray Observatory ObsId 57688
DOI: 10.25574/57691
title: Chandra X-ray Observatory ObsId 57691
DOI: 10.25574/57692
title: Chandra X-ray Observatory ObsId 57692
DOI: 10.25574/57694
title: Chandra X-ray Observatory ObsId 57694
DOI: 10.25574/57695
title: Chandra X-ray Observatory ObsId 57695
DOI: 10.25574/57698
title: Chandra X-ray Observatory ObsId 57698
DOI: 10.25574/57704
title: Chandra X-ray Observatory ObsId 57704
DOI: 10.25574/57713
title: Chandra X-ray Observatory ObsId 57713
DOI: 10.25574/57716
title: Chandra X-ray Observatory ObsId 57716
DOI: 10.25574/57720
title: Chandra X-ray Observatory ObsId 57720
DOI: 10.25574/57723
title: Chandra X-ray Observatory ObsId 57723
DOI: 10.5445/ir/1000150159
title: Numerical experiments to "A unified error analysis for nonlinear wave-type equations with application to acoustic boundary conditions" (Version 3)
DOI: 10.25574/57727
title: Chandra X-ray Observatory ObsId 57727
DOI: 10.25574/57730
title: Chandra X-ray Observatory ObsId 57730
DOI: 10.25574/57733
title: Chandra X-ray Observatory ObsId 57733
DOI: 10.25574/57736
title: Chandra X-ray Observatory ObsId 57736
DOI: 10.25574/57739
title: Chandra X-ray Observatory ObsId 57739
DOI: 10.25574/57741
title: Chandra X-ray Observatory ObsId 57741