cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20063780.v1
title: Table S1. Summary of the main morphotectonic events across the NE Tibetan Plateau

description: Summary of the main morphotectonic events across the NE Tibetan Plateau
DOI: 10.6084/m9.figshare.20063774
title: Conformational changes in the catalytic region are responsible for heat-induced activation of hyperthermophilic homoserine dehydrogenase

description: <strong>Fig. 2: Effect of pH on enzyme activity.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were assayed in 100 mM acetate (pHs4.5, 5,0 and 5.5), MES (pHs 5.5, 6.0 and 6.5), HEPES (pHs 6.5, 7.0, 7.5 and 8.0), Tris (pHs 8.0, 8.5 and 9.0), Glycine (pHs 9.0, 9.5 and 10.0) or CAPS (pHs 10.0, 10.5, 11.0 and 11.5) at 303 K. <br> <strong>Fig. 3: Effect of temperature on enzyme activity.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were assayed in 100 mM tris (pH 8.0) at various temperatures. <br> <strong>Supplementary Figure 1. pH stabilities of the enzymes.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were incubated in 100 mM acetate (pHs 4.5, 5,0 and 5.5), MES (pHs 5.5, 6.0 and 6.5), HEPES (pHs 6.5, 7.0, 7.5 and 8.0), Tris (pHs 8.0, 8.5 and 9.0), Glycine (pHs 9.0, 9.5 and 10.0) or CAPS (pHs 10.0, 10.5, 11.0 and 11.5) at 353 K for 10 min and the residual activities were assayed in 100 mM Tris pH 8.0 at 303 K.
DOI: 10.6084/m9.figshare.20063774.v1
title: Conformational changes in the catalytic region are responsible for heat-induced activation of hyperthermophilic homoserine dehydrogenase

description: <strong>Fig. 2: Effect of pH on enzyme activity.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were assayed in 100 mM acetate (pHs4.5, 5,0 and 5.5), MES (pHs 5.5, 6.0 and 6.5), HEPES (pHs 6.5, 7.0, 7.5 and 8.0), Tris (pHs 8.0, 8.5 and 9.0), Glycine (pHs 9.0, 9.5 and 10.0) or CAPS (pHs 10.0, 10.5, 11.0 and 11.5) at 303 K. <br> <strong>Fig. 3: Effect of temperature on enzyme activity.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were assayed in 100 mM tris (pH 8.0) at various temperatures. <br> <strong>Supplementary Figure 1. pH stabilities of the enzymes.</strong> The activity of the immature (white bar) and mature enzymes (grey bar) were incubated in 100 mM acetate (pHs 4.5, 5,0 and 5.5), MES (pHs 5.5, 6.0 and 6.5), HEPES (pHs 6.5, 7.0, 7.5 and 8.0), Tris (pHs 8.0, 8.5 and 9.0), Glycine (pHs 9.0, 9.5 and 10.0) or CAPS (pHs 10.0, 10.5, 11.0 and 11.5) at 353 K for 10 min and the residual activities were assayed in 100 mM Tris pH 8.0 at 303 K.
DOI: 10.5517/ccdc.csd.cc2bdq7q
title: CCDC 2157173: Experimental Crystal Structure Determination

description: Related Article: Qiang Zhu, Jay Johal, Daniel E. Widdowson, Zhongfu Pang, Boyu Li, Christopher M. Kane, Vitaliy Kurlin, Graeme M. Day, Marc A. Little, Andrew I. Cooper|2022|J.Am.Chem.Soc.|144|9893|doi:10.1021/jacs.2c02653
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5519/qd.z7xbo3es
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (105 records)
DOI: 10.6084/m9.figshare.20063838.v1
title: Additional file 2 of Clinical efficacy of osimertinib in EGFR-mutant non-small cell lung cancer with distant metastasis

description: Additional file 2. Supplementary data.
DOI: 10.6084/m9.figshare.20063838
title: Additional file 2 of Clinical efficacy of osimertinib in EGFR-mutant non-small cell lung cancer with distant metastasis

description: Additional file 2. Supplementary data.
DOI: 10.6084/m9.figshare.20063845
title: Additional file 2 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 2: Table S1. Basic characteristic features of 175 HMT genes identified in wheat.
DOI: 10.6084/m9.figshare.20063848.v1
title: Additional file 3 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 3: Table S2. Location and Homoeologous of HMT genes in wheat.
DOI: 10.6084/m9.figshare.20063851
title: Additional file 4 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 4: Table S3. Distribution of HMT family members in subgroups in wheat, Arabidopsis and rice.
DOI: 10.6084/m9.figshare.20063845.v1
title: Additional file 2 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 2: Table S1. Basic characteristic features of 175 HMT genes identified in wheat.
DOI: 10.6084/m9.figshare.20063848
title: Additional file 3 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 3: Table S2. Location and Homoeologous of HMT genes in wheat.
DOI: 10.6084/m9.figshare.20063851.v1
title: Additional file 4 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 4: Table S3. Distribution of HMT family members in subgroups in wheat, Arabidopsis and rice.
DOI: 10.6084/m9.figshare.20063854
title: Additional file 5 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 5: Table S4. Targeting analysis of miRNAs and HMT genes in wheat.
DOI: 10.6084/m9.figshare.20063854.v1
title: Additional file 5 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 5: Table S4. Targeting analysis of miRNAs and HMT genes in wheat.
DOI: 10.6084/m9.figshare.20063857
title: Additional file 6 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 6: Table S5. mRNA abundance of top 30 highly expressed genes.
DOI: 10.6084/m9.figshare.20063857.v1
title: Additional file 6 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 6: Table S5. mRNA abundance of top 30 highly expressed genes.
DOI: 10.6084/m9.figshare.20063860
title: Additional file 7 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 7: Table S6. Phenotypic statistics. Table S6-1.The number of the tiller. Table S6-2. Thousand seed weight (TSW) (g).
DOI: 10.6084/m9.figshare.20063863
title: Additional file 8 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 8: Table S7. Primers used for qRT-PCR and ChIP-qPCR.
DOI: 10.6084/m9.figshare.20063860.v1
title: Additional file 7 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 7: Table S6. Phenotypic statistics. Table S6-1.The number of the tiller. Table S6-2. Thousand seed weight (TSW) (g).
DOI: 10.6084/m9.figshare.20063863.v1
title: Additional file 8 of Comprehensive molecular evaluation of the histone methyltransferase gene family and their important roles in two-line hybrid wheat

description: Additional file 8: Table S7. Primers used for qRT-PCR and ChIP-qPCR.
DOI: 10.34740/kaggle/dsv/3762906
title: OCRmbi
DOI: 10.6084/m9.figshare.20063513.v2
title: 778.RNAseq.NormCoverage.DESeq2.forward.bw

description: test
DOI: 10.6084/m9.figshare.20063513
title: 778.RNAseq.NormCoverage.DESeq2.forward.bw

description: test
DOI: 10.6084/m9.figshare.20063513.v1
title: 778.RNAseq.NormCoverage.DESeq2.forward.bw

description: 778 RNAseq bigWig track, coveraged normalized using DESeq2 sizeFactor
DOI: 10.34740/kaggle/dsv/3798058
title: EIP1559 Waiting Time
DOI: 10.57770/fh0eqj
title: Replication Data for: I spy with my little eye: Learning Optimal Filters for Cross-Modal Stereo under Projected Patterns

description: Kinect's active sensing strategy is very well suited to produce robust and high-frame rate depth maps for human pose estimation. But the shift to the robotics domain surfaced applications under a wider set of operation condition it wasn’t originally designed for. We see the sensor fail completely on transparent and specular surfaces which are very common to every day household objects. We complement the depth estimate within the Kinect by a cross-modal stereo path that we obtain from disparity matching between the included IR and RGB sensor of the Kinect. We investigate how the RGB channels can be combined optimally in order to mimic the image response of the IR sensor. Our combination method produces depth maps that include sufficient evidence for reflective and transparent objects, and preserves at the same time textureless objects, such as tables or a walls. [See our BMVC'11 paper for details.] However, the method is troubled by interference from the IR projector that is required for the active depth sensing method. We investigate these issues and conduct a more detailed study of the physical characteristics of the sensors. Adapting RGB in frequency domain to mimic an IR image did not yield improved performance. We further propose a more general method that learns optimal filters for cross-modal stereo under projected patterns. From the experimental results we conclude that our pre-filtered, cross-modal, SAD-based stereo vision algorithm profits most from combination in the spatial domain, rather than in the frequency domain.
DOI: 10.5281/zenodo.6112237
title: maxATAC Testing Data

description: Data for testing maxATAC code.
DOI: 10.5281/zenodo.5759192
title: maxATAC Testing Data

description: Data for testing maxATAC code.
DOI: 10.5281/zenodo.6478406
title: maxATAC Blacklist Resources

description: This data upload contains the hg38 resources uses to make the maxATAC blacklist. <strong>Files Included:</strong> ENCFF356LFX.bed: hg38 Blacklist from Anshul curation hg38_maxatac_blacklist.bed: V1 maxATAC blacklist hg38-blacklist.v2.bed: V2 blacklist from ENCODE project + Boyle lab hg38_gaps.bed: UCSC table browser list of annotated gaps hg38_centromeres.bed: UCSC table browser list of annotated centromeres hg38_segmental_dups_chrM.bed: UCSC table browser list of segmental duplications that are specific to chrM <em><strong>hg38_maxatac_blacklist_V2.bed: V2 maxATAC blacklist</strong></em>
DOI: 10.5281/zenodo.6478382
title: maxATAC Blacklist Resources

description: This data upload contains the hg38 resources uses to make the maxATAC blacklist. <strong>Files Included:</strong> ENCFF356LFX.bed: hg38 Blacklist from Anshul curation hg38_maxatac_blacklist.bed: V1 maxATAC blacklist hg38-blacklist.v2.bed: V2 blacklist from ENCODE project + Boyle lab hg38_gaps.bed: UCSC table browser list of annotated gaps hg38_centromeres.bed: UCSC table browser list of annotated centromeres hg38_segmental_dups_chrM.bed: UCSC table browser list of segmental duplications that are specific to chrM <em><strong>hg38_maxatac_blacklist_V2.bed: V2 maxATAC blacklist</strong></em>
DOI: 10.6084/m9.figshare.20063900.v1
title: Direct Patterning of Colloidal Quantum Dots with Adaptable Dual-Ligand Surface

description: Image source data for Fig.1d. Numerical and image source data for Fig.2b-f. Numerical and image source data for Fig.3a-e,h. Numerical source data for Fig.4a,c.
DOI: 10.6084/m9.figshare.20063900
title: Direct Patterning of Colloidal Quantum Dots with Adaptable Dual-Ligand Surface

description: Image source data for Fig.1d. Numerical and image source data for Fig.2b-f. Numerical and image source data for Fig.3a-e,h. Numerical source data for Fig.4a,c.
DOI: 10.57770/ag50yq
title: Replication Data for: Multi-Class Video Co-Segmentation with a Generative Multi-Video Model

description: Video data provides a rich source of information that is available to us today in large quantities e.g. from online resources. Tasks like segmentation benefit greatly from the analysis of spatio-temporal motion patterns in videos and recent advances in video segmentation has shown great progress in exploiting these addition cues. However, observing a single video is often not enough to predict meaningful segmentations and inference across videos becomes necessary in order to predict segmentations that are consistent with objects classes. Therefore the task of video co-segmentation is being proposed, that aims at inferring segmentation from multiple videos. But current approaches are limited to only considering binary foreground/background segmentation and multiple videos of the same object. This is a clear mismatch to the challenges that we are facing with videos from online resources or consumer videos. We propose to study multi-class video co-segmentation where the number of object classes is unknown as well as the number of instances in each frame and video. We achieve this by formulating a non-parametric bayesian model across videos sequences that is based on a new videos segmentation prior as well as a global appearance model that links segments of the same class. We present the first multi-class video co-segmentation evaluation. We show that our method is applicable to real video data from online resources and outperforms state-of-the-art video segmentation and image co-segmentation baselines.
DOI: 10.57770/02zbef
title: Replication Data for: Improving the Kinect by Cross-Modal Stereo

description: Kinect's active sensing strategy is very well suited to produce robust and high-frame rate depth maps for human pose estimation. But the shift to the robotics domain surfaced applications under a wider set of operation condition it wasn’t originally designed for. We see the sensor fail completely on transparent and specular surfaces which are very common to every day household objects. We complement the depth estimate within the Kinect by a cross-modal stereo path that we obtain from disparity matching between the included IR and RGB sensor of the Kinect. We investigate how the RGB channels can be combined optimally in order to mimic the image response of the IR sensor. Our combination method produces depth maps that include sufficient evidence for reflective and transparent objects, and preserves at the same time textureless objects, such as tables or a walls. [See our BMVC'11 paper for details.] However, the method is troubled by interference from the IR projector that is required for the active depth sensing method. We investigate these issues and conduct a more detailed study of the physical characteristics of the sensors. Adapting RGB in frequency domain to mimic an IR image did not yield improved performance. We further propose a more general method that learns optimal filters for cross-modal stereo under projected patterns. From the experimental results we conclude that our pre-filtered, cross-modal, SAD-based stereo vision algorithm profits most from combination in the spatial domain, rather than in the frequency domain.
DOI: 10.5281/zenodo.6633832
title: Historical AR data of SPEAR-LE (ens04)

description: The repository contains the historical scenario simulation (4th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.5281/zenodo.6633831
title: Historical AR data of SPEAR-LE (ens04)

description: The repository contains the historical scenario simulation (4th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.6084/m9.figshare.20064158
title: Additional file 1 of Amplification of LTRs of extrachromosomal linear DNAs (ALE-seq) identifies two active Oryco LTR retrotransposons in the rice cultivar Dongjin

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064158.v1
title: Additional file 1 of Amplification of LTRs of extrachromosomal linear DNAs (ALE-seq) identifies two active Oryco LTR retrotransposons in the rice cultivar Dongjin

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064209
title: Additional file 4 of Delphi study to derive expert consensus on a set of criteria to evaluate discharge readiness for adult ICU patients to be discharged to a general ward—European perspective

description: Additional file 4.
DOI: 10.6084/m9.figshare.20064209.v1
title: Additional file 4 of Delphi study to derive expert consensus on a set of criteria to evaluate discharge readiness for adult ICU patients to be discharged to a general ward—European perspective

description: Additional file 4.
DOI: 10.6084/m9.figshare.20064233
title: Additional file 1 of The unreachable doorbells of South Texas: community engagement in colonias on the US-Mexico border for mosquito control

description: Additional file 1. Supplementary Dataset, raw information of recruitment.
DOI: 10.6084/m9.figshare.20064233.v1
title: Additional file 1 of The unreachable doorbells of South Texas: community engagement in colonias on the US-Mexico border for mosquito control

description: Additional file 1. Supplementary Dataset, raw information of recruitment.
DOI: 10.57770/k5mgbp
title: Replication Data for: Using gamification to create and label photos that are challenging for computer vision and people

description: It would be hard to overstate the importance of Computer Vision (CV), applications of which can be found from self-driving cars, through facial recognition to augmented reality and the healthcare industry. Recent years have witnessed dramatic progress in visual-object recognition, partially ascribable to the availability of labeled data. Unfortunately, recognition of obscure, unclear and ambiguous photos that are taken from unusual angles or distances remains a major challenge, as recently shown by the creation of the ObjectNet [1]. This paper complements that work via a game in which obscure, unclear and ambiguous photos are collaboratively created and labeled by the players, who adopt the role of detectives collecting evidence against in-game criminals. The game rules enforce the creation of images that are challenging to identify for CV and people alike, as a means of ensuring the high quality of players' input.
DOI: 10.6084/m9.figshare.20064259
title: Additional file 1 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064259.v1
title: Additional file 1 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064262
title: Additional file 2 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 2.
DOI: 10.6084/m9.figshare.20064262.v1
title: Additional file 2 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 2.
DOI: 10.6084/m9.figshare.20064268.v1
title: Additional file 3 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 3.
DOI: 10.6084/m9.figshare.20064268
title: Additional file 3 of Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

description: Additional file 3.
DOI: 10.6084/m9.figshare.20064356
title: Additional file 2 of Gain and loss of TASK3 channel function and its regulation by novel variation cause KCNK9 imprinting syndrome

description: Additional file 2: Table S1. KCNK9 variants, impact summary, and ACMG classification.
DOI: 10.6084/m9.figshare.20064356.v1
title: Additional file 2 of Gain and loss of TASK3 channel function and its regulation by novel variation cause KCNK9 imprinting syndrome

description: Additional file 2: Table S1. KCNK9 variants, impact summary, and ACMG classification.
DOI: 10.6084/m9.figshare.20064454
title: Additional file 2 of CancerNet: a unified deep learning network for pan-cancer diagnostics

description: Additional file 2. CpG feature assignement and sample assignment to test, train and validation sets.
DOI: 10.6084/m9.figshare.20064454.v1
title: Additional file 2 of CancerNet: a unified deep learning network for pan-cancer diagnostics

description: Additional file 2. CpG feature assignement and sample assignment to test, train and validation sets.
DOI: 10.6084/m9.figshare.20064478
title: Additional file 3 of Mediating role of sensory differences in the relationship between autistic traits and internalizing problems

description: Additional file 3. Raw data.
DOI: 10.6084/m9.figshare.20064478.v1
title: Additional file 3 of Mediating role of sensory differences in the relationship between autistic traits and internalizing problems

description: Additional file 3. Raw data.
DOI: 10.6084/m9.figshare.20064719
title: Additional file 1 of The protective effects of hyperoside on Ang II-mediated apoptosis of bEnd.3 cells and injury of blood-brain barrier model in vitro

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064719.v1
title: Additional file 1 of The protective effects of hyperoside on Ang II-mediated apoptosis of bEnd.3 cells and injury of blood-brain barrier model in vitro

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064044
title: selectml - supplementary material

description: S1Data-simulated.tar.gz - These are simulated data evaluating prediction accuracy for different traits, number of markers, numbers of samples on different testing scenarios. <br> S2Data-zadoks.tar.gz - These are the raw and degressed data, and deregression script obtained from the AGT for the ZADOKS trait. <br> <br> S3Data-zadoks.tar.gz - These are the raw and degressed data, and deregression script obtained from the AGT for the yellow spot trait. <br>
DOI: 10.6084/m9.figshare.20064044.v1
title: selectml - supplementary material

description: S1Data-simulated.tar.gz - These are simulated data evaluating prediction accuracy for different traits, number of markers, numbers of samples on different testing scenarios. <br> S2Data-zadoks.tar.gz - These are the raw and degressed data, and deregression script obtained from the AGT for the ZADOKS trait. <br> <br> S3Data-zadoks.tar.gz - These are the raw and degressed data, and deregression script obtained from the AGT for the yellow spot trait. <br>
DOI: 10.5519/qd.a2atb87q
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.20064662.v1
title: Additional file 1 of mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064662
title: Additional file 1 of mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

description: Additional file 1.
DOI: 10.6084/m9.figshare.20064797
title: Additional file 8 of mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

description: Additional file 8.
DOI: 10.6084/m9.figshare.20064800.v1
title: Additional file 9 of mirTarRnaSeq: An R/Bioconductor Statistical Package for miRNA-mRNA Target Identification and Interaction Analysis

description: Additional file 9.