cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.jf5tub
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Calycomyza humeralis, Calycomyza humeralis Roser, 1840, Calycomyza humeralis (Roser, 1840), 187517290, Calycomyza humeralis (Roser, 1840), Calycomyza humeralis (Roser, 1840), Calycomyza humeralis (Roser, 1840), Calycomyza humeralis, Calycomyza humeralis, Calycomyza humeralis, Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), 161561246, Calycomyza humeralis (Roser, 1840), 157692871, Calycomyza humeralis (Roser, 1840), Calycomyza humeralis (Roser, 1840), Calycomyza humeralis (Roser 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser 1840), Calycomyza humeralis, Phytobia (Calycomyza) humeralis (von Roser 1840), Calycomyza humeralis (von Roser) von Roser, Dizygomyza (Calycomyza) humeralis (von Roser 1840), Calycomyza humeralis (Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Calycomyza humeralis (von Roser, 1840), Agromyza humeralis Roser, 1840, Agromyza atripes Brischke, 1880, Dizygomyza bellidis, Calycomyza humerella (von Roser, 1840), Calycomyza atripes (Brischke, 1880), Calycomyza bellidis (Kaltenbach, 1873), Agromyza humeralis von Roser, 1840, Agromyza bellidis Kaltenbach, 1873, 187517294, Agromyza bellidis Kaltenbach, 1874, Agromyza bellidis Kaltenbach, 1873, Agromyza humeralis Roser, 1840, Calycomyza bellidis (Kaltenbach, 1873), Agromyza humeralis Roser, 1840, Agromyza bellidis Kaltenbach, 1874, 204278169, 187517292, Agromyza bellidis Kaltenbach, 1873, Agromyza bellidis Kaltenbach, 1874, Agromyza humeralis von Roser, 1840, 161561248, Agromyza humeralis von Roser 1840, Agromyza bellidis Kaltenbach, 1873, Calycomyza humerella (von Roser, 1840), Agromyza atripes Brischke, 1881, 161561251, Agromyza humeralis von Roser, 1840, Agromyza atripes Brischke, 1881, Agromyza humeralis von Roser, 1840, Agromyza bellidis, 161561249, Agromyza humeralis von Roser, 1840, Calycomyza bellidis (Kaltenbach, 1873), Agromyza atripes, Calycomyza atripes (Brischke, 1880), Agromyza atripes Brischke, 1880, Agromyza humeralis Roser, 1840, Agromyza atripes Brischke, 1881, Agromyza atripes Brischke, 1880)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 56 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020690-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11494832
title: Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"

description: This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.

The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.

The structure of the information shared in this repository is as follows:The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.

The material is distributed under a CC-BY 4.0 license.
DOI: 10.5281/zenodo.11494833
title: Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"

description: This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.

The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.

The structure of the information shared in this repository is as follows:The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.

The material is distributed under a CC-BY 4.0 license.
DOI: 10.57992/nmrxiv.p84.s710
title: MS-234
DOI: 10.57992/nmrxiv.p84.s710.d3496
title: MS-234[21]

description: 21
DOI: 10.57992/nmrxiv.p84.s710.d3497
title: MS-234[22]

description: 22
DOI: 10.57992/nmrxiv.p84.s710.d3498
title: MS-234[23]

description: 23
DOI: 10.57992/nmrxiv.p84.s710.d3499
title: MS-234[24]

description: 24
DOI: 10.57992/nmrxiv.p84.s710.d3500
title: MS-234[25]

description: 25
DOI: 10.57992/nmrxiv.p84.s719
title: MS-388
DOI: 10.57992/nmrxiv.p84.s719.d3543
title: MS-388[35]

description: 35
DOI: 10.57992/nmrxiv.p84.s719.d3544
title: MS-388[36]

description: 36
DOI: 10.57992/nmrxiv.p84.s719.d3545
title: MS-388[37]

description: 37
DOI: 10.57992/nmrxiv.p84.s719.d3546
title: MS-388[38]

description: 38
DOI: 10.57992/nmrxiv.p84.s719.d3547
title: MS-388[39]

description: 39
DOI: 10.57992/nmrxiv.p84.s721
title: MS_312
DOI: 10.57992/nmrxiv.p84.s721.d3553
title: MS_312[25]

description: 25
DOI: 10.57992/nmrxiv.p84.s721.d3554
title: MS_312[27]

description: 27
DOI: 10.57992/nmrxiv.p84.s721.d3555
title: MS_312[29]

description: 29
DOI: 10.57992/nmrxiv.p84.s721.d3556
title: MS_312[30]

description: 30
DOI: 10.57992/nmrxiv.p84.s721.d3557
title: MS_312[31]

description: 31
DOI: 10.57992/nmrxiv.p84.s722
title: MS-381
DOI: 10.57992/nmrxiv.p84.s722.d3558
title: MS-381[75]

description: 75
DOI: 10.57992/nmrxiv.p84.s722.d3559
title: MS-381[76]

description: 76
DOI: 10.57992/nmrxiv.p84.s722.d3560
title: MS-381[77]

description: 77
DOI: 10.57992/nmrxiv.p84.s722.d3561
title: MS-381[78]

description: 78
DOI: 10.34740/kaggle/dsv/9801622
title: World Weather Repository ( Daily Updating )
DOI: 10.57992/nmrxiv.p84.s707
title: MS-364-neu
DOI: 10.57992/nmrxiv.p84.s707.d3479
title: MS-364-neu[10]

description: 10
DOI: 10.57992/nmrxiv.p84.s707.d3480
title: MS-364-neu[20]

description: 20
DOI: 10.57992/nmrxiv.p84.s707.d3481
title: MS-364-neu[30]

description: 30
DOI: 10.57992/nmrxiv.p84.s707.d3482
title: MS-364-neu[37]

description: 37
DOI: 10.57992/nmrxiv.p84.s722.d3562
title: MS-381[79]

description: 79
DOI: 10.57992/nmrxiv.p84.s723
title: MS_409
DOI: 10.57992/nmrxiv.p84.s723.d3563
title: MS_409[1]

description: 1
DOI: 10.57992/nmrxiv.p84.s723.d3564
title: MS_409[2]

description: 2
DOI: 10.57992/nmrxiv.p84.s723.d3565
title: MS_409[3]

description: 3
DOI: 10.57992/nmrxiv.p84.s723.d3566
title: MS_409[4]

description: 4
DOI: 10.57992/nmrxiv.p84.s723.d3567
title: MS_409[5]

description: 5
DOI: 10.57992/nmrxiv.p84.s707.d3483
title: MS-364-neu[50]

description: 50
DOI: 10.57992/nmrxiv.p84.s709
title: MS_469
DOI: 10.57992/nmrxiv.p84.s709.d3489
title: MS_469[10]

description: 10
DOI: 10.57992/nmrxiv.p84.s709.d3490
title: MS_469[11]

description: 11
DOI: 10.57992/nmrxiv.p84.s709.d3491
title: MS_469[23]

description: 23
DOI: 10.57992/nmrxiv.p84.s709.d3492
title: MS_469[24]

description: 24
DOI: 10.57992/nmrxiv.p84.s709.d3493
title: MS_469[25]

description: 25
DOI: 10.57992/nmrxiv.p84.s709.d3494
title: MS_469[26]

description: 26
DOI: 10.57992/nmrxiv.p84.s709.d3495
title: MS_469[27]

description: 27
DOI: 10.57992/nmrxiv.p84.s711
title: MS_311
DOI: 10.57992/nmrxiv.p84.s711.d3501
title: MS_311[10]

description: 10
DOI: 10.57992/nmrxiv.p84.s711.d3502
title: MS_311[11]

description: 11
DOI: 10.57992/nmrxiv.p84.s711.d3503
title: MS_311[12]

description: 12
DOI: 10.57992/nmrxiv.p84.s711.d3504
title: MS_311[13]

description: 13
DOI: 10.57992/nmrxiv.p84.s711.d3505
title: MS_311[14]

description: 14
DOI: 10.57992/nmrxiv.p84.s714
title: MS-390
DOI: 10.57992/nmrxiv.p84.s714.d3516
title: MS-390[95]

description: 95
DOI: 10.57992/nmrxiv.p84.s714.d3517
title: MS-390[96]

description: 96
DOI: 10.57992/nmrxiv.p84.s714.d3518
title: MS-390[97]

description: 97
DOI: 10.57992/nmrxiv.p84.s714.d3519
title: MS-390[98]

description: 98
DOI: 10.57992/nmrxiv.p84.s714.d3520
title: MS-390[99]

description: 99
DOI: 10.57992/nmrxiv.p84.s716
title: MS_449
DOI: 10.57992/nmrxiv.p84.s716.d3526
title: MS_449[115]

description: 115
DOI: 10.57992/nmrxiv.p84.s716.d3527
title: MS_449[116]

description: 116
DOI: 10.57992/nmrxiv.p84.s716.d3528
title: MS_449[117]

description: 117
DOI: 10.57992/nmrxiv.p84.s716.d3529
title: MS_449[118]

description: 118
DOI: 10.57992/nmrxiv.p84.s716.d3530
title: MS_449[119]

description: 119
DOI: 10.57992/nmrxiv.p84.s717
title: MS_87_700MHz
DOI: 10.57992/nmrxiv.p84.s717.d3531
title: MS_87_700MHz[10]

description: 10
DOI: 10.57992/nmrxiv.p84.s717.d3532
title: MS_87_700MHz[103]

description: 103
DOI: 10.57992/nmrxiv.p84.s717.d3533
title: MS_87_700MHz[13]

description: 13
DOI: 10.57992/nmrxiv.p84.s717.d3534
title: MS_87_700MHz[14]

description: 14
DOI: 10.57992/nmrxiv.p84.s717.d3535
title: MS_87_700MHz[15]

description: 15
DOI: 10.57992/nmrxiv.p84.s717.d3536
title: MS_87_700MHz[18]

description: 18
DOI: 10.57992/nmrxiv.p84.s717.d3537
title: MS_87_700MHz[24]

description: 24
DOI: 10.57992/nmrxiv.p84.s720.d3548
title: MS-362[10]

description: 10
DOI: 10.57992/nmrxiv.p84.s720.d3549
title: MS-362[11]

description: 11
DOI: 10.57992/nmrxiv.p84.s720.d3550
title: MS-362[12]

description: 12
DOI: 10.15468/dl.s3gzgt
title: Occurrence Download

description: A dataset containing 148 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Faurea macnaughtonii E.Phillips"
 ]
}

 The dataset includes 148 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002302-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u587qg
title: Occurrence Download

description: A dataset containing 985 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Mexico",
 "Country is Brazil",
 "Country is Ecuador",
 "Country is Peru",
 "Country is Bolivia (Plurinational State of)",
 "Country is Costa Rica"
 ]
 },
 "OccurrenceStatus is Present",
 {
 "or" : [
 "TaxonKey is Lithobates catesbeianus (Shaw, 1802)",
 "TaxonKey is Lithobates catesbiana (Shaw, 1802)"
 ]
 }
 ]
}

 The dataset includes 985 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013175-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7rnfnf
title: Occurrence Download

description: A dataset containing 66591 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asclepias L."
 ]
}

 The dataset includes 66591 records from 385 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006303-160118175350007/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bpwv3d
title: Occurrence Download

description: A dataset containing 44599 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "TaxonKey is Turdus iliacus Linnaeus, 1758",
 "Year 2020-2023"
 ]
}

 The dataset includes 44599 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077106-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3ffe6t
title: Occurrence Download

description: A dataset containing 231557 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vaccinium uliginosum L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 231557 records from 577 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077114-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jxnt7y
title: Occurrence Download

description: A dataset containing 1741 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Peromyscus furvus J.A.Allen & Chapman, 1897"
 ]
}

 The dataset includes 1741 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009371-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kj4vgc
title: Occurrence Download

description: A dataset containing 1351 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sternula lorata (Philippi & Landbeck, 1861)"
 ]
}

 The dataset includes 1351 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012576-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f55amc
title: Occurrence Download

description: A dataset containing 353672 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vaccinium vitis-idaea L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 353672 records from 667 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0077099-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.4452815
title: ds-uct-011: Asphalt Concrete: X-Ray micro-CT of sample 01 of asphalt concrete.

description: <strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a full asphalt concrete sample (AC-1), including just the final reconstructions for the three scans (top, middle and bottom of the sample), both with voxel size of 13 μm. Including a micro-CT of the inner core through the middle of the sample with voxel size of 7 μm.<br> .Sample with 3.0% air-void content; diameter and height are 25 mm and 50 mm, respectively.<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers. <strong>Details</strong>:<br> .Tomos (top, middle and bottom) - Voxel size: 13 μm (bin 1); Sample-source: 66.5 mm; Sample-detector: 110.0 mm; Optical magnification: 0.4X; Filter: HE#2; Beam energy: 140 kV; Power: 10 W; Exposure time: 5.0 sec; Projections: 1600.<br> ..top - Sample Y: -44874.3 μm.<br> ..mid - Sample Y: -64761.2 μm.<br> ..bot - Sample Y: -84648.2 μm.<br> .Tomo (middle) - Voxel size: 7 μm (bin 1); Sample-source: 35.0 mm; Sample-detector: 140.0 mm; Optical magnification: 0.4X; Filter: HE#2; Beam energy: 140 kV; Power: 10 W; Exposure time: 7.0 sec; Projections: 1600. <strong>Contents</strong>:<br> ._info_ds-uct-011.md<br> ._info_ds-uct-011.txt<br> .ds-uct-011_asphalt_concrete_1.jpg<br> .ds-uct-011_asphalt_concrete_1_top_13um_16bits.zip<br> .ds-uct-011_asphalt_concrete_1_mid_13um_16bits.zip<br> .ds-uct-011_asphalt_concrete_1_bot_13um_16bits.zip<br> .ds-uct-011_asphalt_concrete_1_mid_7um_16bits.zip
DOI: 10.5281/zenodo.3445396
title: Database for comparison between Hybrid Capture and PCR techniques for HPV-HR detection

description: This database displays the results from the HPV-HR detection with the gold standard technique Hybrid Capture 2 (HC2) and different PCR-based techniques. These results are compared in order to determine the degree of agreement between theses techniques.
DOI: 10.5281/zenodo.4543631
title: Two Excel spreadsheets including eddy covariance and sun-induced chlorophyll fluorescence (SIF) data measured at Yunxiao mangrove flux tower.

description: Two Excel spreadsheets including eddy covariance and sun-induced chlorophyll fluorescence (SIF) data measured at Yunxiao mangrove flux tower: (1) data1_30min.xlsx includes all 30-min data within 297 days (2017/12/22~2018/10/14) for 9 variables (one variable in one sheet), supporting Fig 2, 4, 5, 6, and 7, (2) data2_day.xlsx includes all daily data of the same period for 9 variables in a single sheet, supporting Fig 3 and 5. Contact Xudong Zhu at Xiamen University (xdzhu@xmu.edu.cn) if you have any question.
DOI: 10.6084/m9.figshare.26493205
title: <b>Supporting code and data for Tan et al 2024: Pramel15 facilitates zygotic nuclear DNMT1 degradation and DNA demethylation</b>

description: In mammals, global passive demethylation contributes to epigenetic reprogramming during early embryonic development. At this stage, the majority of DNMT1 protein are excluded from nucleus, which are considered as the primary cause. However, whether the remaining nuclear activity of DNMT1 is regulated by additional mechanisms is unclear. Here, we report that nuclear DNMT1 abundance is finetuned through proteasomal degradation in mouse zygotes. We identify a maternal factor, Pramel15, which targets DNMT1 for degradation via Cullin-RING E3 ligases. Loss of Pramel15 elevates DNMT1 levels in the zygote pronuclei, impaired zygotic DNA demethylation, and cause a stochastic gain of DNA methylation in early embryos. Thus, Pramel15 can modulate the residual level of DNMT1 in the nucleus during zygotic DNA replication, thereby ensuring efficient DNA methylation reprogramming in early embryos.
DOI: 10.6084/m9.figshare.26493205.v2
title: <b>Supporting code and data for Tan et al 2024: Pramel15 facilitates zygotic nuclear DNMT1 degradation and DNA demethylation</b>

description: In mammals, global passive demethylation contributes to epigenetic reprogramming during early embryonic development. At this stage, the majority of DNMT1 protein are excluded from nucleus, which are considered as the primary cause. However, whether the remaining nuclear activity of DNMT1 is regulated by additional mechanisms is unclear. Here, we report that nuclear DNMT1 abundance is finetuned through proteasomal degradation in mouse zygotes. We identify a maternal factor, Pramel15, which targets DNMT1 for degradation via Cullin-RING E3 ligases. Loss of Pramel15 elevates DNMT1 levels in the zygote pronuclei, impaired zygotic DNA demethylation, and cause a stochastic gain of DNA methylation in early embryos. Thus, Pramel15 can modulate the residual level of DNMT1 in the nucleus during zygotic DNA replication, thereby ensuring efficient DNA methylation reprogramming in early embryos.
DOI: 10.6084/m9.figshare.26493205.v1
title: R code used in the NGS data analysis. Data used for plotting are also provided in RData files.

description: In mammals, global passive demethylation contributes to epigenetic reprogramming during early embryonic development. At this stage, the majority of DNMT1 protein are excluded from nucleus, which are considered as the primary cause. However, whether the remaining nuclear activity of DNMT1 is regulated by additional mechanisms is unclear. Here, we report that nuclear DNMT1 abundance is finetuned through proteasomal degradation in mouse zygotes. We identify a maternal factor, Pramel15, which targets DNMT1 for degradation via Cullin-RING E3 ligases. Loss of Pramel15 elevates DNMT1 levels in the zygote pronuclei, impaired zygotic DNA demethylation, and cause a stochastic gain of DNA methylation in early embryos. Thus, Pramel15 can modulate the residual level of DNMT1 in the nucleus during zygotic DNA replication, thereby ensuring efficient DNA methylation reprogramming in early embryos.
DOI: 10.5281/zenodo.820763
title: Landslides From Space - Sucun Landslide, China (28Th October 2016)

description: In October 2016, Typhoon Megi passed over China. Through the heavy rainfall a mass movement was accelerated and about 350'000 cubic metres of debris slided down the hill. This landslide killed 27 people.<br>
<br>
The pre-event acquisition is from 25th March 2016 (Sentinel-2) and the post-event acquisition is from 30th December 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br>
<br>
<em>Contains modified Copernicus Sentinel data (2016)</em>
description: {"references": ["http://blogs.agu.org/landslideblog/2016/10/04/sucun-village-landslide-1/"]}